For benchmarking, we placed a lot of emphasis on distribution learning capabilities because this reflects the training objective of generative models. But we also show how downstream preference optimization can be used to further improve molecular properties.

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These models are equipped with a few new features including:

1. protein side chain modeling
2. adaptive ligand sizes
3. confidence score
4. preference alignment

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The code & camera-ready version of our #ICLR2025 paper on "Multi-domain Distribution Learning for De Novo Drug Design" are now available

📚 Paper: openreview.net/forum?id=g3V...

💻 Code: github.com/LPDI-EPFL/Dr...

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Our paper on computational design of chemically induced protein interactions is out in @natureportfolio.bsky.social. Big thanks to all co-authors, especially Anthony Marchand, Stephen Buckley and Bruno Correia!

t.co/vtYlhi8aQm

Excited about the intersection of AI and molecular sciences? Join us at the 'AI & the Molecular World' track @appliedmldays.bsky.social in Lausanne on Feb 13!

The call for talks is now open: t.co/zisz2g3HLU
(please submit your proposals before January 5th)