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Learning","dateModified":"2023-07-19T00:00:00-05:00","datePublished":"2023-07-19T00:00:00-05:00","@type":"BlogPosting","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature/"},"url":"https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Scientific Discovery in the Age of AI","description":"AI for scientific discovery","dateModified":"2023-08-02T00:00:00-05:00","datePublished":"2023-08-02T00:00:00-05:00","@type":"BlogPosting","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio/"},"url":"https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Future Directions in Network Biology","description":"Geometric deep learning, network biology","dateModified":"2023-09-17T00:00:00-05:00","datePublished":"2023-09-17T00:00:00-05:00","@type":"BlogPosting","@context":"https://schema.org"} + +New papers accepted at NeurIPS | Zitnik Lab + + + + + + + + + + + + + + + + + + + + + + + + + +
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New papers accepted at NeurIPS

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Published: Sep 22, 2023

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Graph AI in Medicine

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Published: Oct 24, 2023

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Structure-Based Drug Design

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Published: Oct 24, 2023

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Next Generation of Therapeutics Commons

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Published: Nov 23, 2023

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We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Coordinator

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Nov 2023:   Next Generation of Therapeutics Commons

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We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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diff --git a/drugml/index.html b/drugml/index.html index 0f9d9e01..a3d4b9c2 100644 --- a/drugml/index.html +++ b/drugml/index.html @@ -96,7 +96,7 @@ - AI Tools + AI Methods diff --git a/feed.xml b/feed.xml index 161b821a..ebefdb46 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -Jekyll2023-09-18T20:28:41-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikFuture Directions in Network Biology2023-09-17T00:00:00-04:002023-09-17T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio<p>Excited to share our perspectives on <a href="https://arxiv.org/abs/2309.08478">current and future directions in network biology.</a></p>Marinka ZitnikExcited to share our perspectives on current and future directions in network biology.Scientific Discovery in the Age of AI2023-08-02T00:00:00-04:002023-08-02T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature<p>New paper on the role of <a href="https://rdcu.be/dinBA">artificial intelligence in scientific discovery is published in Nature.</a></p>Marinka ZitnikNew paper on the role of artificial intelligence in scientific discovery is published in Nature.PINNACLE - Contextual AI protein model2023-07-19T00:00:00-04:002023-07-19T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE<p><a href="https://www.biorxiv.org/content/10.1101/2023.07.18.549602">PINNACLE is a contextual AI model for protein understanding</a> that dynamically adjusts its outputs based on biological contexts in which it operates. <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.Our Group is Joining the Kempner Institute2023-06-21T00:00:00-04:002023-06-21T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/21/Kempner<p>Excited to join <a href="https://news.harvard.edu/gazette/story/newsplus/kempner-institute-announces-appointment-of-four-new-associate-faculty/">Kempner’s inaugural cohort of associate faculty</a> to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.</p>Marinka ZitnikExcited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.Welcoming a New Postdoctoral Fellow2023-06-11T00:00:00-04:002023-06-11T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/11/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=zW32dXsAAAAJ">Shanghua Gao</a> who is joining our group as a postdoctoral research fellow.</p>Marinka ZitnikAn enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.On Pretraining in Nature Machine Intelligence2023-06-01T00:00:00-04:002023-06-01T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/01/Pretraining-NatMachIntelligence<p>Excited to share our new study on <a href="https://www.nature.com/articles/s42256-023-00647-z">language model pretraining and general-purpose methods for biological sequences.</a> <a href="https://zitniklab.hms.harvard.edu/projects/SIPT/">Project website.</a></p>Marinka ZitnikExcited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.Congratulations to Ada and Michelle2023-05-15T00:00:00-04:002023-05-15T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/05/15/CongratsMichelleAda<p>Congrats to PhD student Michelle on being selected as the <a href="http://www.albertjryanfoundation.org/fellows.cfm">2023 Albert J. Ryan Fellow</a> and also to participate in the <a href="https://www.heidelberg-laureate-forum.org/">Heidelberg Laureate Forum</a>. Congratulations to PhD student Ada for being selected as the <a href="https://www.harvard.edu/kempner-institute">Kempner Institute Graduate Fellow</a>!</p>Marinka ZitnikCongrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!Universal Domain Adaptation at ICML 20232023-04-24T00:00:00-04:002023-04-24T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/24/RaincoatICML23<p>New paper introducing <a href="https://arxiv.org/abs/2302.03133">the first model for closed-set and universal domain adaptation on time series</a> accepted at <a href="https://icml.cc/Conferences/2023">ICML 2023</a>. Raincoat addresses feature and label shifts and can detect private labels. <a href="https://zitniklab.hms.harvard.edu/projects/Raincoat/">Project website.</a></p>Marinka ZitnikNew paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.Celebrating Achievements of Our Undergrads2023-04-13T00:00:00-04:002023-04-13T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/13/Undergrads<p>Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological &amp; Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.</p>Marinka ZitnikUndergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological &amp; Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.Welcoming a New Postdoctoral Fellow2023-04-13T00:00:00-04:002023-04-13T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/13/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=LE3ctn0AAAAJ&amp;hl=en">Tianlong Chen</a>, our newly appointed postdoctoral fellow.</p>Marinka ZitnikAn enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow. \ No newline at end of file +Jekyll2023-12-13T09:24:11-06:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikNext Generation of Therapeutics Commons2023-11-23T00:00:00-06:002023-11-23T00:00:00-06:00https://zitniklab.hms.harvard.edu/2023/11/23/OpenPositions<p>We are building the next generation of <a href="https://tdcommons.ai/">Therapeutics Commons</a>! We are seeking <a href="/postdoc-TDC/">outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.</a></p>Marinka ZitnikWe are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.Graph AI in Medicine2023-10-24T00:00:00-05:002023-10-24T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/10/24/GraphAIMedicine<p><a href="https://arxiv.org/abs/2310.13767">Graph AI models in medicine</a> integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.</p>Marinka ZitnikGraph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.Structure-Based Drug Design2023-10-24T00:00:00-05:002023-10-24T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/10/24/SBDD<p><a href="https://arxiv.org/abs/2306.11768">Geometric deep learning has emerged as a valuable tool</a> for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.</p>Marinka ZitnikGeometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.New papers accepted at NeurIPS2023-09-22T00:00:00-05:002023-09-22T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/09/22/NewPapersNeurIPS<p>Congratulations to <a href="https://zitniklab.hms.harvard.edu/people/">Owen and Zaixi</a> for having their papers accepted <a href="https://nips.cc/">as spotlights at NeurIPS</a>! These papers introduce techniques for <a href="https://arxiv.org/abs/2306.02109">explaining time series models through self-supervised learning</a> and <a href="https://arxiv.org/abs/2310.02553">co-designing protein pocket sequences &amp; 3D structures</a>.</p>Marinka ZitnikCongratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences &amp; 3D structures.Future Directions in Network Biology2023-09-17T00:00:00-05:002023-09-17T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio<p>Excited to share our perspectives on <a href="https://arxiv.org/abs/2309.08478">current and future directions in network biology.</a></p>Marinka ZitnikExcited to share our perspectives on current and future directions in network biology.Scientific Discovery in the Age of AI2023-08-02T00:00:00-05:002023-08-02T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature<p>New paper on the role of <a href="https://rdcu.be/dinBA">artificial intelligence in scientific discovery is published in Nature.</a></p>Marinka ZitnikNew paper on the role of artificial intelligence in scientific discovery is published in Nature.PINNACLE - Contextual AI protein model2023-07-19T00:00:00-05:002023-07-19T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE<p><a href="https://www.biorxiv.org/content/10.1101/2023.07.18.549602">PINNACLE is a contextual AI model for protein understanding</a> that dynamically adjusts its outputs based on biological contexts in which it operates. <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.Our Group is Joining the Kempner Institute2023-06-21T00:00:00-05:002023-06-21T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/06/21/Kempner<p>Excited to join <a href="https://news.harvard.edu/gazette/story/newsplus/kempner-institute-announces-appointment-of-four-new-associate-faculty/">Kempner’s inaugural cohort of associate faculty</a> to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.</p>Marinka ZitnikExcited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.Welcoming a New Postdoctoral Fellow2023-06-11T00:00:00-05:002023-06-11T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/06/11/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=zW32dXsAAAAJ">Shanghua Gao</a> who is joining our group as a postdoctoral research fellow.</p>Marinka ZitnikAn enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.On Pretraining in Nature Machine Intelligence2023-06-01T00:00:00-05:002023-06-01T00:00:00-05:00https://zitniklab.hms.harvard.edu/2023/06/01/Pretraining-NatMachIntelligence<p>Excited to share our new study on <a href="https://www.nature.com/articles/s42256-023-00647-z">language model pretraining and general-purpose methods for biological sequences.</a> <a href="https://zitniklab.hms.harvard.edu/projects/SIPT/">Project website.</a></p>Marinka ZitnikExcited to share our new study on language model pretraining and general-purpose methods for biological sequences. 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Machine Learning Foundations

- -Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design and ultimately acquire such knowledge autonomously. +Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design, eventually enabling AI to learn on its own and acquire knowledge autonomously.

-We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in scientific knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. +We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in medical knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization.

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AI for Science | Therapeutic Science

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We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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Oct 2023:   Structure-Based Drug Design

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Graduate students

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Postdoctoral research fellows in foundational AI

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Postdoctoral research fellows and students in AI & therapeutics

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We have multiple openings for postdoctoral research fellows in the broad area of foundation models focusing on geometric deep learning, multimodal learning, large-scale knowledge graphs, large language models, and/or generative AI.

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We are building the next generation of Therapeutics Commons!

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Zitnik Lab founded Therapeutics Commons, a global open-science initiative to access and evaluate AI across therapeutic modalities (small molecules, macro-molecules, cell and gene therapies) and stages of drug discovery (from target discovery, activity modeling, efficacy, and safety, to manufacturing and post-marketing safety monitoring and drug repurposing).

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We are seeking outstanding postdoctoral research fellows and students, machine learning and data specialists, biomedical AI fellows, and an AI community manager who will lead research in AI to advance molecular drug design and clinical drug development.

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Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.

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NOW OPEN: Request For Applications

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Postdoctoral research fellows in foundation AI

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We have multiple openings for postdoctoral research fellows in the broad area of foundation models focusing on geometric deep learning, multimodal learning, large-scale knowledge graphs, large language models, generative AI, and AI agents.

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.

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Postdoctoral research [NOW OPEN: Request For Applications](/postdoc-biomedicalAI-MGB) --> -
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Nov 2023:   Next Generation of Therapeutics Commons

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Sep 2023:   New papers accepted at NeurIPS

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PhD Forum (ECML/PKDD 2020)

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Oct 2023:   Structure-Based Drug Design

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

+ + + + + + +
+ + + +
+
+
@@ -1016,122 +1136,6 @@

PhD Forum (ECML/PKDD 2020)

-
-
- -
- -

Feb 2023:   New Preprint on Distribution Shifts

- - -
- -
- - - -

New preprint on domain adaptation for time series under feature and label shifts. Project website.

- - - - - - -
- - - -
-
- -
-
- -
- -

Feb 2023:   PrimeKG published in Scientific Data

- - -
- -
- - - -

Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

- - - - - - -
- - - -
-
- -
-
- -
- -

Jan 2023:   GNNDelete published at ICLR 2023

- - -
- -
- - - -

New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

- - - - - -
- - - -
-
- -
-
- -
- -

Jan 2023:   New Network Principle for Molecular Phenotypes

- - -
- -
- - - -

New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.

- - - - - -
- - - -
-
-
diff --git a/news/index.html b/news/index.html index 159019ce..bf6d7b03 100644 --- a/news/index.html +++ b/news/index.html @@ -97,7 +97,7 @@ - AI Tools + AI Methods @@ -170,6 +170,126 @@
+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
+ + + +

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

+ + + + + + +
+ + + +
+
+
@@ -1492,118 +1612,6 @@
-
-
- -
- -

Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

- - -
- -
- - - -

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

- - - - - -
- - - -
-
- -
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- -
- -

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

- - -
- -
- - - -

Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

- - - - - -
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Jan 2022:   Inferring Gene Multifunctionality

- - -
- -
- - - -

Hot off the press in Cell Systems. Webster is a tool to infer gene multifunctionality from high-dimensional gene perturbation data by applying sparse representation learning to large CRISPR-Cas9 fitness screens. Explore Webster’s web portal.

- - - - - -
- - - -
-
- -
-
- -
- -

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

- - -
- -
- - - -

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

- - - - - -
- - - -
-
-
+
+
+ +
+ +

Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

+ + +
+ +
+ + + +

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

+ + +
+ +
+ + + +

Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jan 2022:   Inferring Gene Multifunctionality

+ + +
+ +
+ + + +

Hot off the press in Cell Systems. Webster is a tool to infer gene multifunctionality from high-dimensional gene perturbation data by applying sparse representation learning to large CRISPR-Cas9 fitness screens. Explore Webster’s web portal.

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

+ + +
+ +
+ + + +

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

+ + + + + +
+ + + +
+
+
@@ -1469,130 +1581,6 @@
-
-
- -
- -

Jun 2020:   Subgraph Neural Networks

- - -
- -
- - - -

Subgraph neural networks learn powerful subgraph representations that create -fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.

- - - - - -
- - - -
-
- -
-
- -
- -

Jun 2020:   Defense Against Adversarial Attacks

- - -
- -
- - - -

GNNGuard can defend graph neural networks against a variety -of training-time attacks. Remarkably, -GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

- - - - - -
- - - -
-
- -
-
- -
- -

Jun 2020:   Graph Meta Learning via Subgraphs

- - -
- -
- - - -

G-Meta is a meta-learning approach for graphs that quickly adapts -to new prediction tasks using only a handful of data points. G-Meta works in most challenging, -few-shot learning settings and scales to massive interactomics data as we show -on our new Networks of Life dataset comprising of 1,840 networks.

- - - - - -
- - - -
-
- -
-
- -
- -

May 2020:   The Open Graph Benchmark

- - -
- -
- - - -

A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

- - - - - -
- - - -
-
-
+
+
+ +
+ +

Jun 2020:   Subgraph Neural Networks

+ + +
+ +
+ + + +

Subgraph neural networks learn powerful subgraph representations that create +fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jun 2020:   Defense Against Adversarial Attacks

+ + +
+ +
+ + + +

GNNGuard can defend graph neural networks against a variety +of training-time attacks. Remarkably, +GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jun 2020:   Graph Meta Learning via Subgraphs

+ + +
+ +
+ + + +

G-Meta is a meta-learning approach for graphs that quickly adapts +to new prediction tasks using only a handful of data points. G-Meta works in most challenging, +few-shot learning settings and scales to massive interactomics data as we show +on our new Networks of Life dataset comprising of 1,840 networks.

+ + + + + +
+ + + +
+
+ +
+
+ +
+ +

May 2020:   The Open Graph Benchmark

+ + +
+ +
+ + + +

A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

+ + + + + +
+ + + +
+
+
diff --git a/people/index.html b/people/index.html index 218e0843..cda3be76 100644 --- a/people/index.html +++ b/people/index.html @@ -96,7 +96,7 @@ - AI Tools + AI Methods @@ -355,21 +355,21 @@
- +
- <a href=Zaixi Zhang" /> + <a href=Jason Poulos" />
-

Zaixi Zhang

-

Visiting PhD Student

+

Jason Poulos

+

Postdoctoral Fellow
Brigham and Women's Hospital

@@ -384,21 +384,21 @@
- +
- <a href=George Dasoulas" /> + <a href=Jonathan Richard Schwarz" />
-

George Dasoulas

-

Postdoctoral Fellow
Harvard Data Science Fellow

+

Jonathan Richard Schwarz

+

Postdoctoral Fellow

@@ -413,20 +413,20 @@
- +
- <a href=Wanxiang Shen" /> + <a href=Shanghua Gao" />
-

Wanxiang Shen

+

Shanghua Gao

Postdoctoral Fellow

@@ -440,6 +440,61 @@ +
+ + + +
+ + +
+
+ <a href=Tianlong Chen" /> +
+
+ +
+ +

Tianlong Chen

+

Postdoctoral Fellow

+ +

+ +
+
+ + + +
+ + + +
+ + + +
+ + +
+
+ <a href=George Dasoulas" /> +
+
+ +
+ +

George Dasoulas

+

Postdoctoral Fellow
Harvard Data Science Fellow

+ +

+ +
+
+ + + +
@@ -448,20 +503,20 @@
- +
- <a href=Jonathan Richard Schwarz" /> + <a href=Wanxiang Shen" />
-

Jonathan Richard Schwarz

+

Wanxiang Shen

Postdoctoral Fellow

@@ -475,6 +530,9 @@ + + +
@@ -504,23 +562,26 @@ + + +
- +
- <a href=Shanghua Gao" /> + <a href=Lei Huang" />
-

Shanghua Gao

-

Postdoctoral Fellow

+

Lei Huang

+

Visiting PhD Student

@@ -535,21 +596,21 @@
- +
- <a href=Tianlong Chen" /> + <a href=Zaixi Zhang" />
-

Tianlong Chen

-

Postdoctoral Fellow

+

Zaixi Zhang

+

Visiting PhD Student

@@ -562,26 +623,23 @@ - - -
- +
- <a href=Tom Cobley" /> + <a href=Owen Queen" />
-

Tom Cobley

-

Graduate Researcher
MIT

+

Owen Queen

+

Graduate Researcher

@@ -596,21 +654,21 @@
- +
- <a href=Owen Queen" /> + <a href=Tom Cobley" />
-

Owen Queen

-

Graduate Researcher

+

Tom Cobley

+

Graduate Researcher
MIT

@@ -712,20 +770,20 @@
- +
- <a href=Ivy Liang" /> + <a href=Ayush Noori" />
-

Ivy Liang

+

Ayush Noori

Undergraduate Researcher
Harvard

@@ -741,20 +799,20 @@
- +
- <a href=Ayush Noori" /> + <a href=Ivy Liang" />
-

Ayush Noori

+

Ivy Liang

Undergraduate Researcher
Harvard

@@ -771,7 +829,7 @@ -
+
@@ -951,6 +1013,126 @@

Lab alumni

+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
+ + + +

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

+ + + + + + +
+ + + +
+
+
@@ -1429,122 +1611,6 @@

Lab alumni

-
-
- -
- -

Feb 2023:   New Preprint on Distribution Shifts

- - -
- -
- - - -

New preprint on domain adaptation for time series under feature and label shifts. Project website.

- - - - - - -
- - - -
-
- -
-
- -
- -

Feb 2023:   PrimeKG published in Scientific Data

- - -
- -
- - - -

Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

- - - - - - -
- - - -
-
- -
-
- -
- -

Jan 2023:   GNNDelete published at ICLR 2023

- - -
- -
- - - -

New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

- - - - - -
- - - -
-
- -
-
- -
- -

Jan 2023:   New Network Principle for Molecular Phenotypes

- - -
- -
- - - -

New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.

- - - - - -
- - - -
-
-
diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html index bc50e15f..6726fb8f 100644 --- a/postdoc-ML/index.html +++ b/postdoc-ML/index.html @@ -4,16 +4,16 @@ - Postdoctoral Research Fellows in Foundational AI - Zitnik Lab + Postdoctoral Research Fellows in Foundation AI - Zitnik Lab -Postdoctoral Research Fellows in Foundational AI | Zitnik Lab +Postdoctoral Research Fellows in Foundation AI | Zitnik Lab - + @@ -22,11 +22,11 @@ - + +{"url":"https://zitniklab.hms.harvard.edu/postdoc-ML/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Postdoctoral Research Fellows in Foundation AI","description":"geometric deep learning, knowledge-guided AI via large-scale knowledge graph and language models, and generative AI","@type":"WebPage","@context":"https://schema.org"} + +Postdoctoral Research Fellows and Students in AI & Therapeutics | Zitnik Lab + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

Postdoctoral Research Fellows and Students in AI & Therapeutics

+

+ +
+
+
+ + + + +
+
+
+ +
+ + + +
+

Overview

+ +

Prof. Marinka Zitnik invites applications for multiple Postdoctoral Research Fellowship and Research Associate positions at Harvard University.

+ +

We are building the next generation of Therapeutics Commons. Therapeutics Commons is a global open-science initiative aimed at facilitating access and evaluation of AI across therapeutic modalities (including small molecules, macro-molecules, cell and gene therapies) and stages of drug discovery (spanning from molecular design and target nomination to modeling efficacy, safety, manufacturing, and drug repurposing). The Commons lays the foundational groundwork for AI methods to contribute to the development of novel therapies and explores the potential of AI in advancing drug development.

+ +

We are seeking exceptional postdoctoral research fellows and students, machine learning and data specialists, biomedical AI fellows as well as an AI community manager who will lead research in AI to advance molecular drug design and clinical drug development. Our vision is to lay the foundations for AI-based molecular and clinical drug development, ultimately enabling AI to learn on its own and acquire knowledge autonomously through integration with experimental platforms and self-driving labs.

+ +

Selected candidates will spearhead research in foundation models, large-scale knowledge graphs, multimodal learning, and generative AI. A central focus will be on creating universal benchmarks, developing efficient AI agents, and using data-centric approaches to establish new data and evaluation hubs. Additionally, there will be a strong emphasis on leading research, educational, and outreach initiatives in collaboration with both national and international stakeholders to ensure responsible and ethical use of AI in drug development.

+ +

Interested candidates are invited to review our recent publications and research directions.

+ +

Qualifications

+ +

We are seeking highly motivated applicants with backgrounds in one or more of the following areas: efficient machine learning systems, data-centric AI, generative and foundation models, ML benchmarks, and large-scale AI evaluation.

+ +

We are specifically looking for applicants who can demonstrate strong research skills, ideally with a track record of multiple publications in top-tier venues in machine learning and/or scientific/medical journals.

+ +

Candidates must hold a Ph.D. or an equivalent degree in computer science or a closely related field. Outstanding candidates with a Bachelor’s or Master’s degree will be considered. Strong programming skills and practical experience with leading deep learning frameworks are required.

+ +

Experience in applications of AI to molecular and clinical drug development is a strong plus. Successful candidates will have a track record of creating efficient and scalable models and/or datasets that are used by other scientists in the field.

+ +

Application process

+ +

Positions are available immediately and can be renewed annually. Interested applicants should submit the following documents via email to Prof. Zitnik and use the subject line “Postdoctoral Research Fellows and Students in AI & Therapeutics”:

+ +
    +
  • Curriculum Vitae
    • +
  • Links to your GitHub repositories, data and model hubs, and/or open-science initiatives
    • +
  • Two representative publications (preprints are acceptable)
    • +
  • Statement of research (max three pages) describing +
      +
    • Your current research and future research plans
      • +
    • Your expected contributions in creating the next generation of Therapeutics Commons
      • +
    +
    • +
  • Three letters of recommendation (will be solicited after the initial review)
    • +
+ +

We are currently reviewing applications. Interested candidates are encouraged to submit their applications early.

+ +

Advisor

+ +

Marinka Zitnik is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School, Kempner Institute for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance medical problems past the state of the art and open up new opportunities.

+ +

Dr. Zitnik has published extensively in top ML venues, such as NeurIPS, ICLR, ICML, and leading scientific journals, including Nature, Nature Methods, Nature Communications, and PNAS. She has organized numerous workshops and tutorials in the nexus of AI, deep learning, AI4Science and AI4Medicine at leading conferences, where she is also in the organizing committees.

+ +

Her research received best paper and research awards from International Society for Computational Biology, International Conference on Machine Learning, Bayer Early Excellence in Science Award, Amazon Faculty Research Award, Google Faculty Research Scholar Award, Roche Alliance with Distinguished Scientists Award, Sanofi iDEA-iTECH Award, Rising Star Award in Electrical Engineering and Computer Science (EECS), and Next Generation Recognition in Biomedicine, being the only young scientist with such recognition in both EECS and Biomedicine. Dr. Zitnik was named Kavli Fellow 2023 by the National Academy of Sciences.

+ +

Dr. Zitnik is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. She is a member of the Science Working Group at NASA Space Biology. Dr. Zitnik co-founded Therapeutics Data Commons and is the faculty lead of the AI4Science initiative. Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School.

+ +
+ +

Harvard is an Equal Opportunity Employer.

+ +
+
+ +
+

Latest News

+ +
+ +
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
+ + + +

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   Future Directions in Network Biology

+ + +
+ +
+ + + +

Excited to share our perspectives on current and future directions in network biology.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Aug 2023:   Scientific Discovery in the Age of AI

+ + +
+ +
+ + + +

New paper on the role of artificial intelligence in scientific discovery is published in Nature.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jul 2023:   PINNACLE - Contextual AI protein model

+ + +
+ +
+ + + +

PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jun 2023:   Our Group is Joining the Kempner Institute

+ + +
+ +
+ + + +

Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jun 2023:   Welcoming a New Postdoctoral Fellow

+ + +
+ +
+ + + +

An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Jun 2023:   On Pretraining in Nature Machine Intelligence

+ + +
+ +
+ + + +

Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.

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+ + + +
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+ +
+
+ +
+ +

May 2023:   Congratulations to Ada and Michelle

+ + +
+ +
+ + + +

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

+ + + + + + +
+ + + +
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Apr 2023:   Universal Domain Adaptation at ICML 2023

+ + +
+ +
+ + + +

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

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+ + + +
+
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Apr 2023:   Celebrating Achievements of Our Undergrads

+ + +
+ +
+ + + +

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

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+ + + +
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Apr 2023:   Welcoming a New Postdoctoral Fellow

+ + +
+ +
+ + + +

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

+ + + + + + +
+ + + +
+
+ +
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Apr 2023:   New Study in Nature Machine Intelligence

+ + +
+ +
+ + + +

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

+ + + + + + +
+ + + +
+
+ +
+
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Mar 2023:   Precision Health in Nature Machine Intelligence

+ + +
+ +
+ + + +

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

+ + + + + + +
+ + + +
+
+ +
+
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Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

+ + +
+ +
+ + + +

New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.

+ + + + + +
+ + + +
+
+ +
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Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

+ + +
+ +
+ + + +

New study on zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. Check out our project website and TxGNN Explorer.

+ + + + + + +
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+ +
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+ +
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Mar 2023:   GraphXAI published in Scientific Data

+ + +
+ +
+ + + +

Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Feb 2023:   Welcoming New Postdoctoral Fellows

+ + +
+ +
+ + + +

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

+ + + + + + +
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Tweets

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+ + + + + diff --git a/postdoc-biomedicalAI-MGB/index.html b/postdoc-biomedicalAI-MGB/index.html index 97771801..6a000972 100644 --- a/postdoc-biomedicalAI-MGB/index.html +++ b/postdoc-biomedicalAI-MGB/index.html @@ -96,7 +96,7 @@ - AI Tools + AI Methods @@ -180,6 +180,126 @@

Application process

+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
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diff --git a/products/ada_fang/index.html b/products/ada_fang/index.html index dd5699de..aed4660f 100644 --- a/products/ada_fang/index.html +++ b/products/ada_fang/index.html @@ -23,14 +23,14 @@ - + +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ada_fang/"},"url":"https://zitniklab.hms.harvard.edu/products/ada_fang/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ada Fang","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/ada_fang.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ayush_noori/"},"url":"https://zitniklab.hms.harvard.edu/products/ayush_noori/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ayush Noori","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/ayush_noori.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/chirag_agarwal/"},"url":"https://zitniklab.hms.harvard.edu/products/chirag_agarwal/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Chirag Agarwal","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/chirag_agarwal.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/diego_trujillo/"},"url":"https://zitniklab.hms.harvard.edu/products/diego_trujillo/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Diego Trujillo","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/diego_trujillo.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/george_dasoulas/"},"url":"https://zitniklab.hms.harvard.edu/products/george_dasoulas/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"George Dasoulas","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/george_dasoulas.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/guadalupe_gonzalez/"},"url":"https://zitniklab.hms.harvard.edu/products/guadalupe_gonzalez/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Guadalupe Gonzalez","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/guada_gonzalez.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ivy_liang/"},"url":"https://zitniklab.hms.harvard.edu/products/ivy_liang/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ivy Liang","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/ivy_liang.png","@context":"https://schema.org"} + +Jason Poulos | Zitnik Lab + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Jason Poulos

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Postdoctoral Fellow
Brigham and Women's Hospital

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Jason Poulos

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Postdoctoral Fellow
Brigham and Women's Hospital

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+ + + + + diff --git a/products/jonathan_schwarz/index.html b/products/jonathan_schwarz/index.html index feca6b65..4ef7200d 100644 --- a/products/jonathan_schwarz/index.html +++ b/products/jonathan_schwarz/index.html @@ -23,14 +23,14 @@ - + +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/jonathan_schwarz/"},"url":"https://zitniklab.hms.harvard.edu/products/jonathan_schwarz/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Jonathan Richard Schwarz","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/jonathan_schwarz.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/joshua_pan/"},"url":"https://zitniklab.hms.harvard.edu/products/joshua_pan/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Josh Pan","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/josh_pan.jpg","@context":"https://schema.org"} -Huan He | Zitnik Lab +Lei Huang | Zitnik Lab - + - - + + - + - + - - + + +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/lei_huang/"},"url":"https://zitniklab.hms.harvard.edu/products/lei_huang/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Lei Huang","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/lei_huang.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/marinka_zitnik/"},"url":"https://zitniklab.hms.harvard.edu/products/marinka_zitnik/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Marinka Zitnik","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/marinka_zitnik.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/michelle_dai/"},"url":"https://zitniklab.hms.harvard.edu/products/michelle_dai/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Michelle Dai","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/michelle_dai.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/michelle_li/"},"url":"https://zitniklab.hms.harvard.edu/products/michelle_li/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Michelle M. 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Ektefaie","description":"Artificial Intelligence (AI), Biomedical Machine Learning, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/yasha_ektefaie.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/yepeng_huang/"},"url":"https://zitniklab.hms.harvard.edu/products/yepeng_huang/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Yepeng Huang","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/yepeng_huang.png","@context":"https://schema.org"} +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/zaixi_zhang/"},"url":"https://zitniklab.hms.harvard.edu/products/zaixi_zhang/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Zaixi Zhang","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","dateModified":"2023-12-13T09:24:11-06:00","datePublished":"2023-12-13T09:24:11-06:00","@type":"BlogPosting","image":"https://zitniklab.hms.harvard.edu/img/zaixi_zhang.png","@context":"https://schema.org"} + +Combinatorial Prediction of Therapeutic Targets Using a Causally-Inspired Neural Network | Zitnik Lab + + + + + + + + + + + + + + + + + + + + + + + +
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Combinatorial Prediction of Therapeutic Targets Using a Causally-Inspired Neural Network

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+Phenotype-driven drug discovery, as an emerging alternative to target-driven strategies, focuses on identifying compounds that counteract the overall effects of diseases by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for discovering new therapeutic agents. +

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+We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens – a set of therapeutic targets – capable of reversing disease effects. Unlike current methods, which are limited by their reliance on predefined compound libraries, PDGrapher employs a novel combinatorial prediction framework to widen the search scope. +

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+PDGrapher has demonstrated significant improvements in predicting effective perturbagens, as evidenced by our evaluation across four datasets of genetic and chemical interventions. Notably, PDGrapher successfully predicted effective perturbagens in up to 10% additional test samples and ranked known therapeutic targets up to 35% higher than competing methods. +

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+A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally-intensive models traditionally used in phenotype-driven drug discovery. This direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency and effectiveness. The results from our study suggest that PDGrapher could substantially advance phenotype-driven drug discovery, offering a faster, more expansive approach to identifying novel therapeutic compounds. +

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Target-driven drug discovery, predominant since the 1990s, focuses on the design of highly specific compounds against disease-associated targets such as proteins or enzymes.

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  • A prime example of target-driven drug discovery is the development of small molecule kinase inhibitors like Imatinib. Imatinib halts the progression of chronic myeloid leukemia (CML) by inhibiting the BCR-ABL tyrosine kinase, a mutated protein that drives uncontrolled proliferation of leukemic cells in CML patients.
    • +
  • A second notable example are monoclonal antibodies like Trastuzumab which specifically targets the HER2 receptor, a protein overexpressed in certain types of breast cancer. Trastuzumab inhibits cell proliferation while engaging the body’s immune system to initiate an attack against the cancer.
    • +
+ +

These examples illustrate the success of target-driven approaches, yet the past decade has seen a revival of phenotype-driven approaches. This shift has been fueled by the observation that the majority of the first-in-class drugs approved by the US FDA between 1999 and 2008 were discovered empirically without a drug target hypothesis. Instead of the one drug, one gene, one disease model of target-driven approaches, phenotype-driven approaches aim to identify compounds, or perturbagens, that reverse phenotypic disease effects as measured by high-throughput screening (HTS) assays. Notable recent successes include Ivacaftor, the first available therapy treating the underlying cause of cystic fibrosis, and Risdiplam, the first oral medicine approved to treat spinal muscular atrophy.

+ +

Overview of PDGrapher

+ +

Current phenotype-driven lead discovery methods are limited as they identify potential leads by predicting and matching gene expression response signatures with known disease signatures. We propose an alternative approach where the objective is to determine which genes a new perturbagen should target to reverse disease effects as represented by a disease signature. We term this approach combinatorial prediction of therapeutic targets due to its focus on predicting gene combinations.

+ +

The problem of finding which elements of a system should be perturbed to achieve a desired outcome is typically referred to as optimal intervention design within the causal discovery literature. Here, we build on this concept to introduce PDGrapher, an approach for the combinatorial prediction of therapeutic targets to shift cell gene expression from an initial to a treated state in genetic and chemical intervention datasets. Our predictive task is formulated using a causal model, where genes are the nodes in a causal graph and structural equations encode their causal relationships. The goal is to identify a set of genes a perturbagen should target to transition nodes states from diseased to treated.

+ +
+ +
+ +

We consider protein-protein interaction networks (PPI) or gene regulatory networks (GRN) as approximations of the causal graph, assuming no unobserved confounders. We tackle our objective through a representation learning approach, using graph neural network (GNN)-based models to implicitly represent the structural equations. For a given pair of diseased and treated samples, PDGrapher outputs a ranking of genes, with the top-predicted genes identified as the primary therapeutic targets that shift gene expression phenotypes from a diseased to a treated state.

+ +

We evaluate PDGrapher across four datasets of genetic and chemical interventions in held out folds containing novel samples, and held out folds containing novel samples and cell lines:

+ +
    +
  • PDGrapher ranks ground-truth therapeutic targets up to 35% higher in chemical intervention datasets and 16% higher in genetic intervention datasets in held out folds containing novel samples, compared with existing approaches.
    • +
  • In held out folds containing both novel samples and cell lines, PDGrapher maintained its robust performance.
    • +
  • A key innovation of PDGrapher is its ability to directly predict perturbagens that can shift gene expression from diseased to treated states in contrast with prevailing approaches that indirectly predict perturbagens through extensive computational modeling of cell responses. This enables up to 30 times faster training than existing methods.
    • +
  • Therapeutic targets predicted by PDGrapher in chemical intervention datasets agree well with ground-truth therapeutic targets, indicating that PDGrapher can elucidate the mode of action for chemical perturbagens and potentially enhance phenotype-driven lead discovery.
    • +
+ +
+ +
+ +

Publication

+ +

Combinatorial prediction of therapeutic targets using a causally-inspired neural network
+Guadalupe Gonzalez, Isuru Herath, Kirill Veselkov, Michael Bronstein and Marinka Zitnik
+In Review 2023 [bioRxiv]

+ +
@article{gonzalez2023combinatorial,
+  title={Combinatorial prediction of therapeutic targets using a causally-inspired neural network},
+  author={Gonzalez, Guadalupe and Herath, Isuru and Veselkov, Kirill and Bronstein, Michael and Zitnik, Marinka},
+  journal={bioRxiv},
+  url={},
+  year={2023}
+}
+
+ +

Code Availability

+ +

Pytorch implementation of PDGrapher is available in the GitHub repository.

+ +

Authors

+ + + +
+
+ +
+

Latest News

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Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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+
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+ +
+ +

Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
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+ +
+ +

Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Sep 2023:   Future Directions in Network Biology

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Excited to share our perspectives on current and future directions in network biology.

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Aug 2023:   Scientific Discovery in the Age of AI

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New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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Jul 2023:   PINNACLE - Contextual AI protein model

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PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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Jun 2023:   Our Group is Joining the Kempner Institute

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Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

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Jun 2023:   Welcoming a New Postdoctoral Fellow

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An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.

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Jun 2023:   On Pretraining in Nature Machine Intelligence

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Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.

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May 2023:   Congratulations to Ada and Michelle

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Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

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Apr 2023:   Universal Domain Adaptation at ICML 2023

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New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

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Apr 2023:   Celebrating Achievements of Our Undergrads

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Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

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Apr 2023:   Welcoming a New Postdoctoral Fellow

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An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

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Apr 2023:   New Study in Nature Machine Intelligence

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New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

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Mar 2023:   Precision Health in Nature Machine Intelligence

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New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

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Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

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New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.

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Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

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New study on zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. Check out our project website and TxGNN Explorer.

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Mar 2023:   GraphXAI published in Scientific Data

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Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.

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Feb 2023:   Welcoming New Postdoctoral Fellows

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A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

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Tweets

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Authors

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+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

+ + +
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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   New Preprint on Distribution Shifts

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New preprint on domain adaptation for time series under feature and label shifts. Project website.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.

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Authors

+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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+ + + +
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Sep 2023:   New papers accepted at NeurIPS

+ + +
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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   New Preprint on Distribution Shifts

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New preprint on domain adaptation for time series under feature and label shifts. Project website.

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Feb 2023:   PrimeKG published in Scientific Data

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Authors

+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   New Preprint on Distribution Shifts

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New preprint on domain adaptation for time series under feature and label shifts. Project website.

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Feb 2023:   PrimeKG published in Scientific Data

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Jan 2023:   GNNDelete published at ICLR 2023

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Authors

+
+
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+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Authors

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+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
+ + + +

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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Authors

+
+
+ +
+ +

Nov 2023:   Next Generation of Therapeutics Commons

+ + +
+ +
+ + + +

We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Structure-Based Drug Design

+ + +
+ +
+ + + +

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Oct 2023:   Graph AI in Medicine

+ + +
+ +
+ + + +

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

+ + + + + + +
+ + + +
+
+ +
+
+ +
+ +

Sep 2023:   New papers accepted at NeurIPS

+ + +
+ +
+ + + +

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Nov 2023:   Next Generation of Therapeutics Commons

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Nov 2023:   Next Generation of Therapeutics Commons

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We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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Oct 2023:   Structure-Based Drug Design

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Publication

Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik
-In Review 2023 [arXiv]

+Proceedings of Neural Information Processing Systems, NeurIPS 2023 [arXiv]

@inproceedings{queen2023encoding,
 title = {Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency},
 author = {Queen, Owen and Hartvigsen, Thomas and Koker, Teddy and Huan, He and Tsiligkaridis, Theodoros and Zitnik, Marinka},
-booktitle = {rXiv:2306.02109},
+booktitle = {Proceedings of Neural Information Processing Systems, NeurIPS},
 year      = {2023}
 }
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Nov 2023:   Next Generation of Therapeutics Commons

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Oct 2023:   Structure-Based Drug Design

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Nov 2023:   Next Generation of Therapeutics Commons

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Oct 2023:   Structure-Based Drug Design

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Nov 2023:   Next Generation of Therapeutics Commons

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https://zitniklab.hms.harvard.edu/publications/papers/raindrop-iclr22.pdf -2022-03-17T00:07:48-04:00 +2022-03-16T23:07:48-05:00 https://zitniklab.hms.harvard.edu/publications/papers/referralnets-perinatology21.pdf -2021-05-29T22:46:50-04:00 +2021-05-29T21:46:50-05:00 https://zitniklab.hms.harvard.edu/publications/papers/repurposing-covid19-supp.pdf -2021-04-27T23:13:49-04:00 +2021-04-27T22:13:49-05:00 https://zitniklab.hms.harvard.edu/publications/papers/repurposing-covid19.pdf -2021-04-27T23:11:59-04:00 +2021-04-27T22:11:59-05:00 https://zitniklab.hms.harvard.edu/publications/papers/review-inffus19.pdf -2018-10-18T14:50:42-04:00 +2018-10-18T13:50:42-05:00 https://zitniklab.hms.harvard.edu/publications/papers/sieve-bmcbioinf15.pdf -2016-12-18T17:11:10-05:00 +2016-12-18T16:11:10-06:00 https://zitniklab.hms.harvard.edu/publications/papers/skipGNN-scirep20.pdf -2020-12-09T12:19:43-05:00 +2020-12-09T11:19:43-06:00 https://zitniklab.hms.harvard.edu/publications/papers/sub-waveform22.pdf 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https://zitniklab.hms.harvard.edu/publications/slides/raindrop-AAAI22.pdf -2022-02-28T18:17:33-05:00 +2022-02-28T17:17:33-06:00 diff --git a/software/index.html b/software/index.html index e9a7ed53..35327b15 100644 --- a/software/index.html +++ b/software/index.html @@ -96,7 +96,7 @@ - AI Tools + AI Methods @@ -148,6 +148,84 @@
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PDGrapher

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Combinatorial prediction of therapeutic targets using a causally-inspired neural network

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FAIR

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Full-Atom Protein Pocket Design via Iterative Refinement

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Nov 2023:   Next Generation of Therapeutics Commons

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We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   New Preprint on Distribution Shifts

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New preprint on domain adaptation for time series under feature and label shifts. Project website.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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Jan 2023:   New Network Principle for Molecular Phenotypes

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