Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Accurate Ensemble Molecular Dynamics Binding Free Energy Ranking of Multidrug-Resistant HIV-1 Proteases

12 Apr 2010

Abstract excerpt

Accurate calculation of important thermodynamic properties, such as macromolecular binding free energies, is one of the principal goals of molecular dynamics simulations. However, single long simulation frequently produces incorrectly converged quantitative results due to inadequate sampling of conformational space in a feasible wall-clock time. Multiple short (ensemble) simulations have been shown to explore...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Drug Discovery Methods
Share to HIV/AIDS drug development and treatment
Share to Protein Structure and Dynamics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 20384328
DOI: 10.1021/ci100007w

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Accurate Ensemble Molecular Dynamics Binding Free Energy Ranking of Multidrug-Resistant HIV-1 ProteasesPMID 20384328Read ↗

Automated molecular simulation based binding affinity calculator for ligand-bound HIV-1 proteases.Semantic proximity Read ↗

Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations.Semantic proximity Read ↗

Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning.Semantic proximity Read ↗

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.Semantic proximity Read ↗

Rapid, Precise, and Reproducible Prediction of Peptide–MHC Binding Affinities from Molecular Dynamics That Correlate Well with ExperimentSemantic proximity Read ↗

Pose-Rescorer: A Deterministic Single-Frame MM/GBSA Workflow for Post-Docking Ligand RankingSemantic proximity Read ↗

Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular DynamicsSemantic proximity Read ↗

Mutation-Induced Resistance of SARS-CoV-2 Mpro to WU-04 Revealed by Multi-Scale Modeling.Semantic proximity Read ↗

Drug Resistance Mutations Alter Dynamics of Inhibitor-Bound HIV-1 ProteaseSemantic proximity Read ↗

Reliable <i>in silico</i> ranking of engineered therapeutic TCR binding affinities with MMPB/GBSASemantic proximity Read ↗

The folding free-energy surface of HIV-1 protease: insights into the thermodynamic basis for resistance to inhibitors.Semantic proximity Read ↗

Molecular insights from conformational ensembles via machine learningSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.