Skip to content

Latest commit

 

History

History
68 lines (51 loc) · 2.27 KB

README.md

File metadata and controls

68 lines (51 loc) · 2.27 KB
Raw

MolSHAP

Interpreting Quantitative Structure-Activity Relationships using Shapley Values of R-Groups

For details, please refer to our J. Chem. Inf. Model. paper.

If you find this software useful in your work, please cite our paper.

Requirements

  • python = 3.7
  • rdkit = 2021.09.2
  • sklearn = 1.0.1
  • shap = 0.41.0
  • numpy
  • scipy
  • pandas

Pipelines

Step1. Prepare data

A table containing a number of congeneric compounds is required as input. Specifically, each compound shoule be organized as a single row with its ID, SMILES, and Activity as columns. The activity should be in log scale, e.g., pKi, pIC50, -log10[Mol/L]. Note that other properties, if can be decomposed into side chains, can also be used for analyzing. Properties such as T1/2 (time), Tmax (time) are not suitable here.

For example,

ID SMILES Activity
1 CCN1C2=C([C@@H]([C@H](NC(=O)C3=CC=CC=C3)C1=O)C1=CC=C(F)C=C1)C(C)=NN2C1=CC=CC=C1 5.88
2 CCN1C2=C([C@@H]([C@H](NC(=O)C3=CC(C)=CC=C3)C1=O)C1=CC=C(F)C=C1)C(C)=NN2C1=CC=CC=C1 6.70
3 CCN1C2=C([C@@H]([C@H](NC(=O)C3=CC(Cl)=CC=C3)C1=O)C1=CC=C(F)C=C1)C(C)=NN2C1=CC=CC=C1 6.19

Step2. Side-chain decomposition

Run the following script for side-chain decomposition using auto scaffold:

python decompose.py -i ./demo/demo.csv -o ./demo/

You may also specify the scaffold in SMILES format with -c option:

python decompose.py -i ./demo/demo.csv -c O=C1C\(N[*:5]\)C\([*:4]\)c2c\([*:3]\)nn\([*:2]\)c2N1[*:1] -o ./demo/

Note that the special characters (e.g., (, )) in SMILES should be converted using backslash (e.g., \(, \)).

You can modify the scaffold until satisfaction.

Step3. Build model

Run the following script for MolSHAP analysis:

python molshap.py -i ./demo/demo.csv -o ./demo/

Outputs

filename description
prefix_decomp.pk Decomposition results
prefix_frag2idx.pk Mapping from fragment SMILES to index
prefix_contrib.pk Side-chain fragment contributions by index
prefix_contrib_by_smiles.pk Side-chain fragment contributions by SMILES
prefix_opt.csv Table of optimized compounds
prefix_opt.png Scatter plot of optimized compounds