Roehrig, U. F. | bioRxiv Science

bioRxiv2026

Attracting Cavities 3.0: Faster and More Versatile Molecular Docking for the SwissDock Webserver

MotivationMolecular docking is a pillar of structure-based drug design and shows advantages in structure prediction of small-molecule ligand-protein complexes over co-folding methods for novel ligands and novel binding pockets. Here, we describe substantial improvements of our physics-based docking algorithm Attracting Cavities, which is widely used through the SwissDock webserver. ResultsAC 3.0 includes enhanced sampling features, new functionalities, and technical improvements. These lead to better sampling at lower execution times and higher versatility. Comparison with AutoDock Vina demonstrates better docking results on multiple test sets. AvailabilityAC 3.0 will be made available free of charge through the SwissDock webserver (www.swissdock.ch).

bioinformatics↗

bioRxiv2025

Comparative Assessment of the Utility of Co-Folding and Docking for Small-Molecule Drug Design

Deep-learning based co-folding methods predict the structures of proteins interacting with metal ions, small molecules, nucleic acids, peptides, and other proteins. One of their main objectives is their application for drug design, predicting the structure of small-molecule ligand/protein complexes. It has been shown that at present these models memorize ligand poses from the training data but do not generalize effectively to novel complexes and lack in the adherence to physical and chemical principles. Here, we use the recently introduced Runs N Poses benchmark set of 2,600 protein-ligand systems annotated by their similarity to the training data, to show that the physics-based docking algorithms Attracting Cavities and AutoDock Vina outperform co-folding methods for novel ligands and novel binding pockets. In addition to predicting ligand poses and at variance with co-folding methods, they provide a physical rationale on why a ligand binds (or does not bind) and insight into experimental structural model deficiencies.

bioinformatics↗