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