Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8325 - 8335· 1 citation· 48 references
Medicine
TL;DR
A quantitative framework for understanding how docking performance responds to methodological improvements has been lacking is developed by modeling large-scale experiments from three previously published docking campaigns, providing an objective basis for benchmarking and comparing virtual screening approaches.
Abstract
Large library docking has emerged as a productive approach for ligand discovery, yet a quantitative framework for understanding how docking performance responds to methodological improvements has been lacking. Here, we develop such a framework by modeling large-scale experiments from three previously published docking campaigns, in which 2,682 ligands had been synthesized and tested across the scoring landscape (poor scores, mediocre scores, high scores). The observed experimental hit-rate curves can be reproduced by a simple bivariate normal distribution model, where docking score is interpreted as a noisy predictor of binding free energy. To account for the plateauing and subsequent drop in hit rates often seen at highly favorable docking scores, we add a term for high-ranking docking artifacts, a phenomenon we observe across targets. From this model, three predictions about the sensitivity of docking performance emerge. First, even slight improvements in scoring accuracy would substantially improve both hit rates and hit affinities: quantitatively, a 0.1 increase in the correlation between docking score and binding affinity would justify accepting a ∼10-fold increase in computational cost per molecule, arguing for reinvestment in scoring function accuracy in library docking. Second, docking artifacts, while hard to anticipate, can come to dominate top-scoring lists as libraries grow. Physically testing molecules across a range of log-normalized ranks (pProp) is therefore essential to identify the peak hit rate for a given campaign. Third, prefiltering a library to enrich for molecules with appropriate physicochemical features increases the intrinsic hit rate and substantially boosts docking performance, particularly at tera-scale, with effects comparable to a meaningful improvement in scoring accuracy. Beyond docking, the model’s parameters (affinity distribution, score-affinity correlation, artifact frequency) can be fit to any screening method with sufficient experimental data, providing an objective basis for benchmarking and comparing virtual screening approaches. These findings offer a practical framework for optimizing large-scale virtual screening as chemical libraries continue to grow.
Physics-based protein–ligand docking critically depends on efficient pose sampling, yet established sampling and local refinement algorithms can be inefficient and unstable in the highly nonconvex energy landscapes characteristic of protein–ligand interactions. To address this limitation, we introduce an enhanced local optimization strategy based on curved line search (CLS) and integrate it into AutoDock Vina, resulting in Vina_CLS. The proposed method enables more flexible step-size selection during local refinement and improves convergence in challenging regions of the energy landscape. Across benchmarks on the PDBbind refined set and the LEADS-PEP data set, Vina_CLS consistently outperforms the baseline, exhibiting greater robustness by solving more docking problems, as well as improved efficiency through reduced function and gradient evaluations and shorter runtimes. These gains translate into practical benefits, including more frequent identification of difficult-to-access local minima, enhanced redocking accuracy, and increased recovery of near-native poses. Together, these results demonstrate that improved local optimization can substantially enhance docking performance, highlighting an important, underexplored opportunity to advance structure-based drug discovery.
Leo Gaskin, Matthias Welsch, J. Kirchmair et al.· Journal of Chemical Theory a...· 0 citations
Molecular docking is one of the most established methods in computational drug discovery, due to its balance of speed and accuracy. However, the accuracy of docking results depends on a number of different parameters, and systematic reference data for comparisons to more advanced methods for binding affinity prediction are still scarce. This study assesses the impact of key parameters on the accuracy of binding free energy estimates from docking, using nine benchmark systems with 278 high-affinity ligands. Using the Molecular Operating Environment (MOE), we evaluated combinations of three receptor structures (two crystal structures, one AlphaFold2 model), two force fields, two scoring functions, two receptor flexibility settings, and two statistical evaluation schemes. The performance of the docking approaches is measured based on the squared Pearson’s correlation coefficient (R²), the root mean square error (RMSE) with respect to the experimental binding affinities, as well as the mean signed error (MSE) and Kendall’s tau for individual targets and the full dataset. The results show that the scoring function and the protein structure are the most important factors for binding affinity accuracy in rigid docking with the MOE software. Amber10:EHT and MMFF94x force fields had the same average Rmean2 value, but Amber10:EHT had a lower average RMSEmean. AlphaFold2 protein models yielded lower binding affinity accuracy and higher errors compared to experimental crystal structures, although induced fit docking improved results. Using the original benchmark, we also compared several docking programs. DOCK6 and MOE performed best, with mean R² values of about 0.49 and 0.40, respectively. The remaining docking programs did not outperform a molecular weight regression baseline. For a subset of four targets (CDK2, JNK1, P38, TYK2) evaluated in previous work, the performance of the optimized DOCK6 and MOE protocols produced correlation coefficients similar to those reported for certain MM/PBSA, FMO, and Boltz2 implementations evaluated on the same target subset. This raises questions about potential dataset biases, the structural preparation, or the implementation of those methods. Docking therefore should be considered as an important and computationally inexpensive reference baseline for binding affinity prediction.
Konstantinos Tornesakis, J. Essex, Paul A. Cox et al.· Journal of Computer-Aided Mo...· 0 citations
This work establishes a practical framework for low-throughput, cost-constrained discovery campaigns capable of delivering chemically tractable binders with favorable property profiles, and introduces a suite of ADMET models for kinetic solubility, lipophilicity, and Caco-2 permeability to improve developability at the point of selection.
Assessment of pose prediction methods when the bound structure of a reference ligand is known and the likely binding mode(s) of a related compound are needed, and this work focuses on cases where the new compound has multiple potential binding modes.
Ažbeta Kubincová, S. S. Çınaroğlu, Jianna Ongsioco et al.· Journal of Chemical Informat...· 0 citations
Structural investigation of re-docked actives showed that re-ranked poses were more native-like, with improved binding-site occupancy, reduced centroid displacement, and greater recovery of co-crystal interactions.
Lai Hoang Son Le, Thanh-An Pham, Ngoc Nguyen Tran et al.· bioRxiv· 0 citations