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<span class="blog-description">A nook to hoard my manuscripts.</span>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-12-11-summary-doi.org/10.1186/s13321-020-00441-8.html" rel="bookmark" title="Permalink to 文献总结｜基于SMILES用于从头药物设计的深度分子骨架改造模型">文献总结｜基于SMILES用于从头药物设计的深度分子骨架改造模型</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 12月11日">2022年 12月11日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
本文介绍于 2020 年发表在 Journal of Cheminformatics 上的一篇文章，文章原标题为 SMILES‑based deep generative scaffold decorator for de‑novo drug design，文章通过 MMP 算法得到训练数据，基于 RNN 模型构建了能对分子骨架进行改造的生成模型，模型能够生成具有指定改造位点的分子骨架，并使用分子片段对其改造从而得到新分子。
</p>
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<aside class="post-tags">
<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/rnn.html"># RNN</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-12-03-summary-doi.org/10.1038/s41467-022-34692-w.html" rel="bookmark" title="Permalink to 文献总结｜通过生成深度学习发现 RIPK1 抑制剂">文献总结｜通过生成深度学习发现 RIPK1 抑制剂</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 12月03日">2022年 12月03日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
本文介绍于 2022 年发表在 Nature Communications 上的一篇文章，文章原标题为 Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor，文章通过构建 CRNN 模型，使用迁移学习、正则化强化和取样强化三种策略针对 RIPK1 靶点生成化合物，并从中得到了潜在的 RIPK1 抑制剂。
</p>
<div class="post-excerpt">
<aside class="post-tags">
<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/crnn.html"># CRNN</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-12-01-install-cuda.html" rel="bookmark" title="Permalink to 为深度学习环境配置 CUDA">为深度学习环境配置 CUDA</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 12月01日">2022年 12月01日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
最近在折腾 OpenNMT 框架，CUDA 与 PyTorch 都是需要的依赖。虽然网络上已经有大量的 CUDA 安装教程，相信也比我写得全面且详细，但手头上的服务器正巧没有安装 CUDA，索性记录下来以备不时之需。
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<a href="https://leonis.cc/tag/linux.html"># Linux</a><a href="https://leonis.cc/tag/cuda.html"># CUDA</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-11-25-summary-doi.org/10.1038/s42256-022-00494-4.html" rel="bookmark" title="Permalink to 文献总结｜使用课程学习改进从头药物设计">文献总结｜使用课程学习改进从头药物设计</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 11月25日">2022年 11月25日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
本文介绍于 2022 年发表在 Nature Machine Intelligence 上的一篇文章，文章原标题为 Improving De Novo Molecular Design with Curriculum Learning，文章介绍了深度学习中的课程学习，并将课程学习的策略用于 REINVENT 模型，相比于强化学习的策略，课程学习对解决复杂任务具有更好效果。
</p>
<div class="post-excerpt">
<aside class="post-tags">
<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/rnn.html"># RNN</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-11-20-summary-doi.org/10.1021/acs.jcim.1c00469.html" rel="bookmark" title="Permalink to 文献总结｜LibINVENT：可用于虚拟化合物库构建的基于反应的分子骨架改造模型">文献总结｜LibINVENT：可用于虚拟化合物库构建的基于反应的分子骨架改造模型</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 11月20日">2022年 11月20日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
本文介绍于 2022 年发表在 Journal of Chemical Information and Modeling 上的一篇文章，文章原标题为 LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design，文章基于 RNN 与 RL 构建了能够对分子进行结构改造的模型，模型在经过分子片段数据的训练后，模型能够在分子相应改造位点上连接不同化学结构，是构建虚拟化合物库的有利工具。
</p>
<div class="post-excerpt">
<aside class="post-tags">
<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/rnn.html"># RNN</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-11-14-summary-doi.org/10.1039/d0sc03126g.html" rel="bookmark" title="Permalink to 文献总结｜SyntaLinker：使用 Conditional Transformer 神经网络自动连接分子片段">文献总结｜SyntaLinker：使用 Conditional Transformer 神经网络自动连接分子片段</a>
</h2>
<span class="post-meta">
<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 11月14日">2022年 11月14日</time>
| Archived in
<a class="post-meta-tag" href="https://leonis.cc/category/sui-sui-nian.html">碎碎念</a>
</span>
<p class="post-excerpt">
本文介绍于 2020 年发表在 Chemical Science 上的一篇文章，文章原标题为 SyntaLinker: automatic fragment linking with deep conditional transformer neural networks，文章将基于语义模型 Transformer 识别并连接分子片段，最终设计了一种分子片段连接工具 SyntaLinker，它能够有效完成有约束的分子片段连接任务。
</p>
<div class="post-excerpt">
<aside class="post-tags">
<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/transformer.html"># Transformer</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-11-05-summary-doi.org/10.1021/acs.jcim.2c00838.html" rel="bookmark" title="Permalink to 文献总结｜使用强化学习和基于图的深度生成模型进行从头药物设计">文献总结｜使用强化学习和基于图的深度生成模型进行从头药物设计</a>
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<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 11月05日">2022年 11月05日</time>
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本文介绍于 2022 年发表在 Journal of Chemical Information and Modeling 上的一篇文章，文章原标题为 De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models，文章借鉴了 REINVENT 模型的强化学习策略，将其用于基于图的分子生成模型，得到了较好的结果。
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<a href="https://leonis.cc/tag/literature-summary.html"># Literature Summary</a><a href="https://leonis.cc/tag/cadd.html"># CADD</a><a href="https://leonis.cc/tag/gnn.html"># GNN</a><a href="https://leonis.cc/tag/rl.html"># RL</a> </aside>
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-10-29-summary-doi.org/10.1021/acs.jmedchem.2c01179.html" rel="bookmark" title="Permalink to 文献总结｜ChemistGA：用于实际药物发现的可合成分子生成算法">文献总结｜ChemistGA：用于实际药物发现的可合成分子生成算法</a>
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<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 10月29日">2022年 10月29日</time>
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本文介绍于 2022 年发表在 Journal of Medicinal Chemistry 上的一篇文章，文章原标题为 ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery，文章改进了传统的遗传算法，使其在药物分子生成的任务上具有比深度学习更优的表现效果。
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-10-21-summary-doi.org/10.1186/s13321-019-0393-0.html" rel="bookmark" title="Permalink to 文献总结｜通过随机化 SMILES 序列提升分子生成模型质量">文献总结｜通过随机化 SMILES 序列提升分子生成模型质量</a>
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<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 10月21日">2022年 10月21日</time>
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本文介绍于 2019 年发表在 Journal of Cheminformatics 上的一篇文章，文章原标题为 Randomized SMILES strings improve the quality of molecular generative models。
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<h2 class="post-title"><a href="https://leonis.cc/sui-sui-nian/2022-10-20-rdkit-draw-molecule.html" rel="bookmark" title="Permalink to RDKit 入门：绘制分子图像">RDKit 入门：绘制分子图像</a>
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<a class="post-meta-tag" href="https://leonis.cc/author/leo.html">Leo</a>
| <time datetime="2022年 10月20日">2022年 10月20日</time>
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介绍使用 RDKit 实现分子可视化的基本操作，主要使用了 rdMolDraw2D 模块，其功能包括分子结构的绘制以及在分子结构上添加标记。
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