Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8269 - 8283· 0 citations· 59 references
Medicine
TL;DR
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.
Abstract
The quality of protein-ligand binding affinity prediction is often limited by the accuracy of positioning the ligand correctly inside the binding site. However, pose accuracy is a secondary concern in high-throughput virtual screening, which is the application scenario in mind when most docking protocols are developed. On the other hand, similar protocols are also applied to position ligands in the binding pocket prior to free-energy calculations, and the accuracy of docking protocols is not well known in this case, especially when structures of analogues are available as templates to guide the docking as is typical in lead optimization. Docking benchmarks typically focus on the ability of docking methods to generate poses with a low RMSD to crystal structures, but for physics-based affinity prediction methods, we also need the ability to identify potential alternate binding modes of a new ligand that might be viable. This is especially relevant for ligand modifications which break local symmetry, leading to multiple potential substituent orientations, such as substitutions of phenyl rings. Here, our focus is on assessment of pose prediction methods when the bound structure of a reference ligand is known (typical in structure-based drug design) and the likely binding mode(s) of a related compound are needed, and we focus on cases where the new compound has multiple potential binding modes. To assess templated docking protocols on their ability to identify binding modes when starting from a solid reference structure, we collected a set of 60 complex structures from the PDB which have more than one ligand binding mode – shown in the PDB records as alternative locations in the ligand. Our results suggest success rates of only 30-50% for finding alternative binding modes, which are modest compared to benchmarks of the same docking programs on the Astex diverse set (70-90% success rates). Overall, we conclude that docking methods would benefit from further tuning or improvement to become more effective in lead optimization.
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
Blind docking is a method for predicting a binding mode of a ligand with a protein without any prior information about a binding site. Some tools allow this type of docking experiment directly, others, including some established tools, require binding site information being passed as an input. In this latter case, one can use cavity prediction tools and use the results of their prediction as an input in these docking calculations. However, it is still unclear if the results of these predictions can be reliably used in protein–ligand docking and what is the best technical way to pass this information to the docking algorithm. In this study we estimated the applicability of the binding pocket prediction tools in docking experiments to address this gap in knowledge. We use four different computational tools for cavity prediction and use the best predicted cavities represented in different ways to run GOLD docking calculations. Analysis of subsequent use in docking highlights that Fpocket and CAVIAR are the best performing cavity prediction tools in this context. Further analysis shows that accurate binding site input does not guarantee accurate binding pose predictions and, even with the predicted cavities, the more restrained the input is, the more reliable the docking results are.
Diana A Kondinskaia, Bojana Popovic· Journal of Computational Che...· 0 citations
Scaffold-Guided Structure Refinement is presented, leveraging information on known binders within ligand series targeting a specific protein, based on the observation that shared molecular scaffolds among binders exhibit conserved binding modes.
J. Pletzer-Zelgert, Matthias Rarey, Bernd Kuhn· Journal of Medicinal Chemist...· 0 citations
Correctly positioning water molecules at the drug-target interface is a major challenge in structure-based drug design. Predicting how water structure reorganizes during drug docking into the target pocket is particularly difficult. Previously, a molecular dynamics-based protocol, HydroDock, was introduced and tested for docking small ligands into hydrated ion channels. Here, we extend the applicability of HydroDock to challenging targets with open binding pockets, populated by highly mobile water molecules that are the most troublesome for drug design. HydroDock improved docking accuracy, with increases of 28% (10%) in median structural (and ranking) performance. The comparison with other methods showed that HydroDock is a robust solution, even for problematic systems in which docking without explicit water molecules failed to find the correct drug-binding mode.
Bayar Bayarsaikhan, C. Hetényi, B. Z. Zsidó· ACS Physical Chemistry Au· 0 citations
The interface prediction program WHISCY is presented, which combines surface conservation and structural information to predict protein–protein interfaces and demonstrates the potential of using interface predictions to drive protein–protein docking.
Sjoerd J. de Vries, A. V. van Dijk, A. M. Bonvin· 0 citations