Bento: Benchmarking Classical and AI Docking on Drug Design–Relevant Data

Marina A. Pak, Daria Frolova, Sergei A. Nikolenko, Talgat Daulbaev, Daria Ryabchenko, Anna Litvin, Pavel Gurevich, Kamil Garifullin, Alexander Shapeev, Ivan Oseledets, Dmitry N. Ivankov

Abstract: h3>Abstract</h3> <p>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 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) careful dataset curation is essential – filtering by pocket structural similarity and controlling ligand complexity exposes generalization failures that are obscured in conventional benchmarks; 2) classical and deep learning-based docking tools perform similarly well on drug-like ligands, making them comparably useful for virtual screening, with physics-based methods offering a clear advantage in speed; 3) co-folding tools outperform other approaches on structurally complex ligands, whereas most methods achieve similar accuracy on regular small molecules; and 4) all methods struggle to generalize to unseen pockets, with deep learning models being the most prone to overfitting. Overall, our results show that while current docking and DL-based approaches are reliable for many drug-design-relevant scenarios, genuine pocket-level generalization remains an open challenge. Bento provides a rigorous and transparent framework for diagnosing these limitations and guiding the development of more robust protein-ligand prediction models. The data and code of Bento are available at https://github.com/LigandPro/Bento.</p>

External IDs:doi:10.64898/2025.12.30.696741