This initiative aligns with the FDA's recent announcement to phase out traditional animal testing in favor of more human-relevant methods. Our collective work provides a roadmap for integrating AI, ensuring safer and more efficient drug development processes.

We spoke to government experts from the National Institute of Environmental Health Sciences (NIEHS) and thought leaders from academia, including the University of Cambridge, the Broad Institute of MIT, and Harvard. This unprecedented industry-wide effort outlines a unified framework for adopting AI.

It’s a strategic blueprint, introducing pillars for success in the real world, drawing from the expertise of scientists from pharma, including GSK, Novartis, Eli Lilly and Company, Merck, AstraZeneca, Bayer | Crop Science, Pfizer, Recursion, Novo Nordisk, Sanofi, Relay Therapeutics, and others.

Yesss 🫢

It so sad! Please tell me the cat survived?

Many thanks to all authors — John Arevalo, Zhenwen Liang, Anne Carpenter, Meng Jiang, and Shantanu Singh!

We look forward to continuing to advance the field of molecular representation learning with. -omics datasets!

Find out more!
github.com/liugangcode/...

By combining information bottleneck methods with context graphs, we’re able to extract minimal yet sufficient representations of molecules that lead to better predictions and generalization in downstream tasks like molecular property prediction and zero-shot molecule-morphology matching.

In this work, Gang Liu introduces InfoAlign, a new approach for learning molecular representations from cellular response data, integrating features like cell morphology and gene expression.

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