Automated Mechanistic Interpretability of Generative AI Models Trained on Biological Data

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Develop automated approaches for mechanistic interpretability of virtual neural network models trained on biological state data. This would enable the extraction of mechanistic insights from predictive models, potentially informing both basic science and therapeutic design.

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Advances in interpretability and our understanding of neural computation

Whitepapers and Essays

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Cellular state is a multifaceted and complex phenomenon, involving multiple overlapping omics layers that vary in time and space. Capturing and representing this multimodal complexity is essential for predictive modeling of cell behavior and for advancing our understanding of cellular function.

Automated Mechanistic Interpretability of Generative AI Models Trained on Biological Data

Resources (1)

R&D Gaps (1)

Cellular and Biomolecular States Are Highly Multimodal and Complex