Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery

Description

This repository contains all code used in our study.

• Abstract
• Modules
• Requirements
• Installation
• Usage
• How to cite
• License

Abstract

Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, either in terms of size or molecular diversity. Active deep learning has high potential for low-data drug discovery, as it allows iterative model improvement during the screening process. However, there are several “known unknowns” that limit the wider adoption of active deep learning in drug discovery: (a) what the best computational strategies are for chemical space exploration, (b) how active learning holds up to traditional, non-iterative, approaches, and (c) how it should be used in the low-data scenarios typical of drug discovery. To provide answers, this study simulates a low-data drug discovery scenario, and systematically analyses six active learning strategies combined with two deep learning architectures, on three large-scale molecular libraries. We identify the most important determinants of success in low-data regimes and show that active learning can achieve up to a six-fold improvement in hit discovery compared to traditional screening methods.

Modules

• data_prep.py: Processes data and clusters compounds for sampling diversity.
• nn.py: Contains neural network models (MLP, GCN, etc.).
• screening.py: Core script for active learning cycles.
• utils.py: Utility functions for data handling and evaluation.
• main.py: Entry point for running experiments with customizable parameters.

Requirements

This codebase uses Python 3.9 and primarily depends on:

• PyTorch (2.0.1)
• PyTorch Geometric (2.3.1)
• RDKit (2023.3.2)
• Scikit-learn (1.3.0)

Tested on Ubuntu 22.04.3 and macOS 13.3.1

Installation

Install dependencies from the provided env.yaml file. This typically takes a couple of minutes (tested on Ubuntu 22.04.3).

conda env create -f env.yaml

Manual installation of requirements (tested on Ubuntu 22.04.3 and macOS 13.3.1):

conda create -n traversing_chem python=3.9
conda activate traversing_chem
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
conda install pyg -c pyg
pip3 install scikit-learn==1.3.0 rdkit==2023.3.2 pandas tqdm argparse h5py

Usage

Data

Before running the active learning pipeline on the same data used in the paper, the original data from LITPCBA needs to be processed.

1. run python active_learning/preprocess_data.py to process and cluster all data. This takes around an hour on a regular computer. Clustering requires >128GB of RAM.

Alternatively, pre-processed data (~40GB) can be found here

Running the pipeline

2. Run python main.py with desired command-line arguments to start the active learning process. Ensure necessary data files are present.

Demo

1. Run python experiments/preprocess_demo.py
2. Run python experiments/demo.py -o demo_results.csv -acq bald -arch mlp -dataset DEMO

After several minutes, this should produce a csv with mock screening results.

Replicate results

Change the paths in experiments/replicate_all_results.sh and run it. Running all experiments will take several thousand hours using an NVIDIA A100 GPU (40GB), but this script is easily modified to run in parallel.

How to cite

You can currently cite our preprint

Traversing Chemical Space with Active Deep Learning: A Computational Framework for Low-data Drug Discovery. Derek van Tilborg and Francesca Grisoni. ChemRxiv, 2023. DOI: https://doi.org/10.26434/chemrxiv-2023-wgl32-v3

License

All code is under MIT license.