Title: Bento: Benchmarking Classical and AI Docking on Drug Design–Relevant Data
Primary area: datasets and benchmarks
Keywords: molecular docking, co-folding, protein-ligand interaction, drug design

Abstract: Recent advances in artificial intelligence have introduced deep learning and co-folding approaches for predicting protein-ligand complexes, raising the question of their applicability and how they compare with classical docking methods. In this work, we present a thorough benchmarking study of eleven tools for protein-ligand interaction prediction, spanning classical molecular docking methods, deep learning-based models, and co-folding algorithms. While most related benchmarking efforts primarily assess the generalization capacity, we extend the analysis to also evaluate the performance on drug design-relevant data and across different classes of protein-ligand complexes. Here, we introduce \textsc{Bento}, a comprehensive benchmark that evaluates 11 tools for protein-ligand interaction prediction -- both established and recently developed -- across four test datasets and multiple derived subsets in a pocket-aware setup. We show that 1) deep learning and co-folding-based methods for protein-ligand prediction struggle on protein pockets that are distinct from common training sets, whereas physics-aware tools remain more robust; 2) for typical drug design scenarios, predictions are sufficiently accurate for practical use, with the best performance achieved by the physics-augmented Gnina, followed by AlphaFold3 and other co-folding methods, which perform on par with classical docking; 3) for a comprehensive evaluation, it is essential not only to use diverse and challenging datasets but also to include homogeneous subsets and drug design-relevant data.

TL;DR: AI protein-ligand interaction prediction methods are comparable to physics-aware docking for drug-design material but fail to generalize to unseen protein pockets, with Gnina emerging as the most reliable performer.