Home Projects torchdrug
torchdrug
Python

torchdrug

A powerful and flexible machine learning platform for drug discovery

by DeepGraphLearning · GitHub
Stars
Forks
License
Created
Last commit
Language
deep-learningdrug-discoverygraph-neural-networksApache-2.0Python
View on GitHub
In plain words

Build machine learning models for drug discovery using a user-friendly platform that supports graph operations.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
torchdrug — GitHub preview card
📈 Star history
1 5841 583
2026-07-202026-08-31
📈 Track torchdrug

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

A powerful and flexible machine learning platform for drug discovery

torchdrug has 1.6k stars on GitHub. It has been forked 220 times. torchdrug is written mainly in Python. It has been in active development since 2021. torchdrug is available under the Apache-2.0 license. Its main topics are deep-learning, drug-discovery, graph-neural-networks, pytorch.

Frequently asked questions

What is torchdrug?

A powerful and flexible machine learning platform for drug discovery

Is torchdrug open source?

torchdrug is an open-source project. It is released under the Apache-2.0 license.

Is torchdrug free?

Yes. torchdrug is free and open source — you can use, modify and self-host it.

What license does torchdrug use?

torchdrug is available under the Apache-2.0 license.

What language is torchdrug written in?

torchdrug is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — torchdrug

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=deepgraphlearning-torchdrug)](https://olud.ai/project/deepgraphlearning-torchdrug.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.