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.
@article{arxiv.2202.08320,
title = {TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery},
author = {Zhaocheng Zhu and Chence Shi and Zuobai Zhang and Shengchao Liu and Minghao Xu and Xinyu Yuan and Yangtian Zhang and Junkun Chen and Huiyu Cai and Jiarui Lu and Chang Ma and Runcheng Liu and Louis-Pascal Xhonneux and Meng Qu and Jian Tang},
journal= {arXiv preprint arXiv:2202.08320},
year = {2022}
}