CENsible

CENsible uses deep-learning context explanation networks (CENs) to predict small-molecule binding affinities. Rather than predict a binding affinity directly, it predicts the contributions of pre-calculated terms to the overall affinity, thus providing interpretable output. CENsible insights are useful for subsequent lead optimization.

Installation

We recommend using pixi to reproduce out Python virtual environments. This will painlessly handle installing the appropriate Python, PyTorch, and CUDA versions. Once you have pixi installed and the repository cloned, all you have to do is run the following command.

pixi install

Once that is finished, you can activate the new Python virtual environment.

pixi shell

To test the installation, run the following command.

./test_predict.sh

Usage

Simple Use

Here is a simple example of how to use CENsible for inference (prediction):

python predict.py --ligpath censible/data/test/1wdn_ligand.mol2 \
                  --recpath censible/data/test/1wdn_receptor.pdb \
                  --smina_exec_path /usr/local/bin/smina \
                  --obabel_exec_path /usr/local/bin/obabel

NOTE: You should replace the --ligpath and --recpath arguments with the path to your ligand and receptor files, respectively. You should also replace the --smina_exec_path and --obabel_exec_path arguments with the paths to your smina and obabel executables, respectively.

Saving CENsible Weights

In the above simple example, CENsible only outputs the predicted affinity. If you also wish to output CENsible's predicted weights (as well as other information used to calculate the final score), use the --tsv_out flag:

python predict.py --ligpath censible/data/test/1wdn_ligand.mol2 \
                  --recpath censible/data/test/1wdn_receptor.pdb \
                  --smina_exec_path /usr/local/bin/smina \
                  --obabel_exec_path /usr/local/bin/obabel \
                  --tsv_out test_out.tsv

CENsible will output affinity to test_out.tsv, an Excel-compatible tab-delimited file. It will also output the following additional information:

• A text description of the pre-calculated terms the model uses.
• The pre-calculated terms themselves (calculated using smina).
• The pre-calculated terms after scaling/normalization.
• The weights the model assigns to each pre-calculated term.
• The predicted contribution of each pre-calculated term to the overall affinity (i.e., the product of the normalized pre-calculated term and its weight).

NOTE: The final affinity is the sum of the predicted contributions.

Saving Per-Atom Gaussian Steric Terms to a PDB File

CENsible can also output the per-atom contributions associated with smina's Gaussian steric (atom_type_gaussian) terms to a PDB file. Use the --pdb_out flag. For example:

python predict.py --ligpath censible/data/test/1wdn_ligand.mol2 \
                  --recpath censible/data/test/1wdn_receptor.pdb \
                  --smina_exec_path /usr/local/bin/smina \
                  --obabel_exec_path /usr/local/bin/obabel \
                  --tsv_out test_out.tsv \
                  --pdb_out test_out.pdb

The per-atom terms are placed in the beta column. See the HEADER fields of the output PDB file for additional useful information.

Using Other CENsible Models

CENsible comes with a pre-trained model (described in the accompanying manuscript, see censible/data/model_allcen3/). If you wish to use your own model, specify the path to the model directory using the --model_dir flag:

python predict.py --ligpath censible/data/test/1wdn_ligand.mol2 \
                  --recpath censible/data/test/1wdn_receptor.pdb \
                  --smina_exec_path /usr/local/bin/smina \
                  --obabel_exec_path /usr/local/bin/obabel \
                  --model_dir ./my_model_dir/ \
                  --tsv_out test_out.tsv

The model directory should contain the following files:

• model.pt: The trained model.
• precalc_term_scales.npy: The pre-calculated term scales.
• which_precalc_terms_to_keep.npy: The pre-calculated terms the model uses.

Citation

If you use CENsible in your work, please cite the following literature.

Bhatt, R., Koes, D. R., & Durrant, J. D. (2024). CENsible: interpretable insights into small-molecule binding with Context Explanation Networks. Journal of Chemical Information and Modeling, 64(12), 4651-4660. DOI: 10.1021/acs.jcim.4c00825

License

This project is released under the GPL-3.0-only License as specified in LICENSE.md.