ArticleJournal of chemical information and modeling2022
Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- AI-enhanced adaptive virtual screening of large libraries for ligand discovery.Nature biotechnology · 2026Article
- Benchmarking Docking Protocols on Predicting Alternative Binding Modes.Journal of chemical information and modeling · 2026Article
- Evaluation of 3',4'-Di-Biomolecules · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
- Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD).bioRxiv : the preprint server for biology · 2026Article
- Integrative Computational Approaches for the Discovery of Triazole-Based Urease Inhibitors: A Machine Learning, Virtual Screening, and Meta-Dynamics Framework.International journal of molecular sciences · 2025Article
- The Discovery of Small ERK5 Inhibitors via Structure-Based Virtual Screening, Biological Evaluation and MD Simulations.Molecules (Basel, Switzerland) · 2025Article
- Interconversion of the (+)-O-desmethyltramadol to the lowest-energy conformer when coupled to µ-opioid receptor: comprehensive analysis using in silico molecular modeling.Journal of computer-aided molecular design · 2025Article
- A putative binding model of nitazene derivatives at the μ-opioid receptor.Neuropharmacology · 2025Article
- The B22 Dilemma: Structural Basis for Conformational Differences in Proinsulin B-Chain Arg22 Mutants.Biomolecules · 2025Article
- Discovery of compound 1105486 as a selective inhibitor of B4GALT1: potential for pancreatic cancer therapy.Frontiers in chemistry · 2025Article
- Computational prediction of CNP0387675 as a non-nucleoside inhibitor of MraY, from natural product-based multi-template screening againstFrontiers in microbiology · 2025Article
- Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.Journal of chemical theory and computation · 2024Article
- Absolute Binding Free Energies with OneOPES.The journal of physical chemistry letters · 2024Article
- Systematical Mutational Analysis of FRATtide against Osteoclast Differentiation by Alanine Scanning.ACS medicinal chemistry letters · 2024Article
- Article
- Computational Investigations on Reaction Mechanisms of the Covalent Inhibitors Ponatinib and Analogs Targeting the Extracellular Signal-Regulated Kinases.International journal of molecular sciences · 2023Article
- OneOPES, a Combined Enhanced Sampling Method to Rule Them All.Journal of chemical theory and computation · 2023Article
- A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.Protein science : a publication of the Protein Society · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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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.