Docking Showdown: Physics-Based, Co-Folding, and Hybrid Models Face Off
- Compares physics-based docking, deep-learning co-folding, and hybrid rescoring methods on how well they generalize to unfamiliar protein targets and chemical scaffolds.
- Builds on the recent Runs-n-Poses benchmark, which found that classical physics-based docking held an edge over several co-folding tools on truly novel targets.
- Assesses pose accuracy and ranking performance to offer practical guidance on choosing a docking strategy based on target novelty.
💡 Points
Physics-based docking still beating co-folding tools on genuinely novel targets is the finding I keep coming back to — these deep-learning models are pattern-matching PDB coverage, not solving folding physics, and it shows the moment a target has no close analog. Until a co-folding tool proves itself on a zero-analog case, I'm running both and trusting the classical scorer as the tiebreaker.
Multiparadigm Benchmark of Molecular Docking: From Physics to Co-Folding and Hybrid Models ↗