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Mapping gene–gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene–gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.

Engage in a real-world challenge

Develop new methods to improve our ability to derive gene–gene interaction networks from single-cell perturbational data
Work with a real-world dataset and contribute to a key challenge in the use of machine learning for drug discovery

Build a team to innovate and inspire

We call on all machine-learning researchers and engineers eager to contribute to impactful problems in drug discovery
Take part as an individual or as a team – team size is unrestricted but up to three individuals is encouraged

How to get involved

Visit our GitHub starter repository to get started with your implementation
Develop new models and algorithms to improve two existing graph inference methods leveraging interventional data
Submit a short report describing your solution, challenges faced and insights gained (see task description below for format)

Submission and next steps

Submission details are provided in the README in the GitHub repository: https://github.com/causalbench/causalbench-starter
If your method makes it to the top of the leader board, you will have the opportunity to present your work at a top machine-learning event - the Workshop on Machine Learning for Drug Discovery at ICLR 2023 – and to win prizes from our sponsors

View previous contests

GeneDisco Challenge

Best of luck, may the best team win!

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