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

By Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yang Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu and 5 more. 2022

88 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jiahua Rao
2. Jian Tang
3. Payel Das
4. Vijil Chenthamarakshan
5. Arian Jamasb
6. Shuangjia Zheng

In Environmental Chemistry

1. Jian Tang

In Materials Chemistry

1. Jiahua Rao
2. Jian Tang
3. Payel Das
4. Vijil Chenthamarakshan
5. Arian Jamasb
6. Shuangjia Zheng

Citations in the corpus

• TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “The drug compound is represented as a graph using TorchDrug toolkit [42].”
• Protein Representation Learning by Geometric Structure Pretraining (2022, methodology): “We adopt the implementation in TorchDrug (Zhu et al., 2022), where 6 GCN layers with the hidden dimension of 512 are used.”