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Kinase Specifc Fingerprints

This Repository accompanys our work on: "Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-based Virtual Screening."

https://pubs.acs.org/doi/10.1021/acs.jcim.0c01208?

The provided code can be used to generate Molecular Fingerprints that are specific to Kinases. While in the paper we explore many different architectures, here we only use a MLP trained for multitask prediction to generate the neural fingerprints. This selection was made due to the fact that is was the best perfoming fingerprint.

Requirements

  • Python 3.7
  • Pytorch 1.6
  • rdKit 2019.03.4

A yml file containing all requirements is provided. This can be easily setup using conda.

Installation

  1. Download the Repository

  2. Create and Activate Conda Environment: Navigate to the Folder containing the environment.yml

    conda env create -f environment.yml
conda activate get_nnfp_env

Generate Fingerprints

You can use a csv file containing a column with SMILES strings as input to our model. Naviagte to *your path*/kinase_nnfp/code and run:

python get_fp.py ../data/example_data.csv -s smiles

smiles is the name of the column containing the SMILES strings.

You can also provide the Index of the column containing the SMILES.

python get_fp.py ../data/example_data.csv -s 0

If you do not have a header add the -n flag

python get_fp.py ../data/example_data.csv -s 0 -n

Perform a Similarity Seach based on the produced Fingerprint

If you want to use the NNFPs for a similarity search make sure that the query is in the same file as the molecules you want to screen before you generate the Fingerprints.

With python simsearch.py *path of fingerprintfile* -q *index of the query* the similairty search can be performed

Given you generated fingerprints for the example_data.csv. The following code will perform a similarity search for the query with index 0

python simsearch.py ../data/nnfp_output.csv -q 0

You can also perform a similarity search for multiple queries by adding addtional indices.

python simsearch.py ../data/nnfp_output.csv -q 0 15 8 1 84

A new folder will be generated containing the results of the similairty search Like in the original paper, the cosine similarity is used for the search.