prof-ajay-jain.bsky.social
@prof-ajay-jain.bsky.social
Reposted
Looks like yet another case of overhyped results due to poor #bioMLeval evaluation of deep learning models -this time deep docking methods - specifically DiffDock. Look forward to the DiffDock authors response. But dont see any major flaws in this critique. Conclusion is REALLY worth reading!
Been a while since I read a paper like this:
• "What [DiffDock] appears to be doing cannot be considered" docking
• "Results are ... contaminated with near neighbors to test cases"
• "Results for DiffDock were artifactual"
• "Results for other methods were incorrectly done"
arxiv.org/abs/2412.02889
Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows
The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, wi...
arxiv.org
December 5, 2024 at 11:30 PM
Thanks Derek! Ann (@annclevesjain.bsky.social) and I enjoyed your summary as well as your discussion of the linkage to similar observations other high-profile deep-learning reports!
DiffDock, a new diffusion-based ligand docking program, made s big splash earlier this year. But it’s apparently not all it’s claimed to be:
Computational Care
www.science.org
December 6, 2024 at 11:48 PM