Top Tweets for #bench3dfit

Pocket-conditioned ligand generation is like finding the right key for a lock. 🔐 The protein pocket is the lock 🔒, and the ligand is the key 🔑. The key needs the right shape and size to fit into the pocket without colliding with its walls. But fitting the pocket is only the first part of the challenge. 📐 The teeth of the key need to line up with the right parts of the lock - just like a ligand needs to position the right functional groups at the right locations to make specific interactions with protein residues. In practice, chemists specify these spatial requirements using different abstractions. An anchor fragment 🧩 is like a part of the molecule that you already know has to be there. A pharmacophore point 🎯 marks where a particular type of chemical feature needs to be positioned. An interaction ⚡ represents a specific contact the ligand needs to make with the pocket. Diffusion models 🌊 have traditionally dominated 3D molecular generation, but integrating multiple heterogeneous conditions is often non-trivial. LLMs 🤖 offer a compelling alternative: they can naturally combine these diverse 3D instructions through a common language interface. We explore this in 3D-Fit 🧬, a benchmark for 3D ligand generation under realistic spatial constraints. The results show that modern foundation models can follow local 3D constraints surprisingly well, but satisfying these constraints does not always translate into physically plausible poses or strong docking scores. Read more in the #Bench3DFit paper: https://t.co/yTs2OFstV8 Explore recent results in #DDDBench: https://t.co/oYrb7wpEcS