  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.
