ArticleJournal of chemical information and modeling2024
Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Evaluating molecular docking for binding affinity predictions: a systematic analysis of key parameters and the utility of AlphaFold2 structures for the Schrödinger dataset.Journal of computer-aided molecular design · 2026Article
- Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.Journal of chemical information and modeling · 2026Article
- NEMAT: An Automated Nonequilibrium Free-Energy Framework for Predicting Ligand Affinity in Membrane Proteins.Journal of chemical information and modeling · 2026Article
- Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.Journal of chemical theory and computation · 2026Article
- Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction.Briefings in bioinformatics · 2026Article
- The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.Journal of chemical information and modeling · 2026Review
- Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site.Journal of chemical information and modeling · 2026Article
- A Safer Alternative Bio-Repellent: Targeting Mosquito Odorant-Binding Proteins with Catnip-Derived Nepetalactones fromInternational journal of molecular sciences · 2026Article
- BindFlow: A Free, User-Friendly Pipeline for Absolute Binding Free Energy Calculations Using Free Energy Perturbation or MM(PB/GB)SA.Journal of chemical theory and computation · 2026Article
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
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- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- Evaluation of the ABCG2 Charge Model in Protein-Ligand Binding Free-Energy Calculations.Journal of chemical information and modeling · 2025Article
- Rapid, accurate, and reproduciblemSphere · 2025Article
- Scaffold Hopping Method for Design and Development of Potential Allosteric AKT Inhibitors.Molecular biotechnology · 2025Article
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- Advancing Binding Affinity Calculations: A Non-Equilibrium Simulations Approach for Calculation of Relative Binding Free Energies in Systems with Trapped Waters.Journal of chemical theory and computation · 2025Article
- Comparative Analysis of Quantum-Mechanical and Standard Single-Structure Protein-Ligand Scoring Functions with MD-Based Free Energy Calculations.Journal of chemical information and modeling · 2025Article
- Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy.Journal of chemical information and modeling · 2025Article
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Authors and funding
5 authors.
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Abstract
In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.