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.

翻译：在本文中,我们介绍化学X,这是一家以PyTorrch为基础的深层学习图书馆,旨在提供一系列最新艺术模型,以解决药物配对的评分任务。图书馆的主要目标是使机器学习研究人员和从业者在精简的框架内能够利用深药配对的评分模型。 化学X再利用现有高水平示范培训设施的设计、几何深学习和PyToch生态系统的深化学层。我们的系统为最终用户提供神经网络层、定制对口评分结构、数据装载器和批量复制器。我们用样例代码片和案例研究来展示这些特征,以突出化学X的特征。关于真实世界药物相互作用、多药方效应和综合协同预测任务的一系列实验表明,化学X中现有的模型在解决对口评分任务方面是有效的。最后,我们证明化学X可用于在大型药物配对数据集上培训和评分机器学习模型,并配有数十万种商品硬件的化合物。