That's true 😁 We have kernel density estimate plots and statistical tests in Appendix (Fig. 18 and 19)

While DrugFlow is really good in distribution learning, we also show that it can be easily tuned to generate molecules with optimised target properties. For example, we can boost docking efficiency and synthetic accessibility, or reduce the level of swill in samples @wpwalters.bsky.social

Our adaptive size mechanism allows DrugFlow to remove excessive atoms on the fly, adjusting the molecular size to the provided pocket constraints. In our experiments, we demonstrate that DrugFlow can avoid steric clashes if the provided molecular size is too big.

DrugFlow's atom-level confidence score is able to detect unlikely samples and correlates with molecular size and docking efficiency. In particular, DrugFlow tends to assign low confidence in cases when it introduces steric clashes with the protein or produces strange geometries.

🚨 Check out DrugFlow, our new generative model for structure-based drug design. DrugFlow provides an atom-level confidence score for each designed molecule, and can adjust molecular size on the fly!

Additional details in thread 🧵

#ICLR2025

(2/2) SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints openreview.net/forum?id=uvH...

(1/2) Multi-domain Distribution Learning for De Novo Drug Design openreview.net/forum?id=g3V...

Really excited: 2/2 our papers got accepted at #ICLR2025! Thanks to all amazing co-authors and hope to see you in Singapore! 🇸🇬

If you're interested in generative models and RL agents for drug design check our papers in the thread 🧵

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)