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Machine learning has huge potential to revolutionize the field of drugdiscovery and is attracting increasing attention in recent years. However,lacking domain knowledge (e.g., which tasks to work on), standard benchmarksand data preprocessing pipelines are the main obstacles for machine learningresearchers to work in this domain. To facilitate the progress of machinelearning for drug discovery, we develop TorchDrug, a powerful and flexiblemachine learning platform for drug discovery built on top of PyTorch. TorchDrugbenchmarks a variety of important tasks in drug discovery, including molecularproperty prediction, pretrained molecular representations, de novo moleculardesign and optimization, retrosynthsis prediction, and biomedical knowledgegraph reasoning. State-of-the-art techniques based on geometric deep learning(or graph machine learning), deep generative models, reinforcement learning andknowledge graph reasoning are implemented for these tasks. TorchDrug features ahierarchical interface that facilitates customization from both novices andexperts in this domain. Tutorials, benchmark results and documentation areavailable at https://torchdrug.ai. Code is released under Apache License 2.0.

TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery