ChemicalX: A Deep Learning Library for Drug Pair Scoring
This library addresses the problem of drug pair scoring for machine learning researchers and practitioners in drug discovery, but it is incremental as it builds on existing PyTorch utilities and layers.
The authors introduced ChemicalX, a PyTorch-based deep learning library for drug pair scoring, which provides state-of-the-art models and tools to make this task accessible to researchers and practitioners, demonstrating effectiveness in real-world tasks like drug-drug interaction prediction.
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners in a streamlined framework.The design of ChemicalX reuses existing high level model training utilities, geometric deep learning, and deep chemistry layers from the PyTorch ecosystem. Our system provides neural network layers, custom pair scoring architectures, data loaders, and batch iterators for end users. We showcase these features with example code snippets and case studies to highlight the characteristics of ChemicalX. A range of experiments on real world drug-drug interaction, polypharmacy side effect, and combination synergy prediction tasks demonstrate that the models available in ChemicalX are effective at solving the pair scoring task. Finally, we show that ChemicalX could be used to train and score machine learning models on large drug pair datasets with hundreds of thousands of compounds on commodity hardware.