Few-Shot Compound Activity Prediction (FS-CAP)

This repository contains code for the few-shot compound activity prediction (FS-CAP) algorithm.

Docker instructions

First add a trained model file to the same folder as the Dockerfile (or use the one provided here, which was trained with 8 context compounds), then run sudo docker build -t fscap . to build the container, then run sudo docker run fscap along with any command line arguments. For example, sudo docker run fscap --context_smiles "<smiles>" --context_activities <activities> --query_smiles "<smiles>".

Requirements

RDKit is required. All code was tested in Python 3.10. The following pip packages are also required:

torch
scipy
scikit-learn
numpy
tqdm

Preprocessing

We only provide code to preprocess BindingDB for training, but testing on other datasets using a trained model should be relatively straightforward using the score_compounds.py script.

BindingDB

preprocess_bindingdb.py contains code to extract and preprocess data from BindingDB. Calling python preprocess_bindingdb.py will load data from BindingDB_All.tsv which should be placed in the folder beforehand, and after running it will produce a bindingdb_data.pickle file that is ready for training. For the paper, we used BindingDB_All.tsv from BindingDB's Download page, available here.

Training

train.py contains the main script to train FS-CAP. By default, the model will train with 8 context compound and will save tensorboard logs to the logs folder. After training, it will save the model file to model.pt. Other model hyperparameters can be found and adjusted in the config variable in the train.py file.

Inference

score_compounds.py uses the trained model to perform inference on a given set of context and query compounds. The following parameters must be supplied: --context_smiles specifies the SMILES strings of the context molecules, separated by semicolons (e.g. CCC;CCCC;CCCCC), and --context_activities specifies the associated activites in nanomoles/liter (nM) (e.g. 1000;1;10 if the activites are 1000 nM, 1 nM, and 10 nM, respectively). --query_smiles specifies the SMILES string(s) of the query molecule(s) (if multiple, separate with semicolons), --model_file specifies the path to the trained model (default model.pt), and encoding_dim specifies the encoding_dim parameter used in training (default 512). The script prints to stdout the activity prediction of the query molecule(s) in nM, one prediction per line.