ArticleJournal of chemical theory and computation2014
Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors.
Article in Journal of chemical theory and computation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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Who cites it
55 citing papers in PubMed, 145 citations in OpenAlex.
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- Ensemble molecular dynamics for predicting binding energies of DNA intercalators.RSC advances · 2026Article
- MnM-W-MMGBSA: A Computational Strategy to Improve Relative Binding Free Energies of Protein-Protein Interaction Systems.The journal of physical chemistry. B · 2025Article
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- Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Impact of Varying Velocities and Solvation Boxes on Alchemical Free-Energy Simulations.Journal of chemical information and modeling · 2025Article
- Equilibrium and Nonequilibrium Ensemble Methods for Accurate, Precise and Reproducible Absolute Binding Free Energy Calculations.Journal of chemical theory and computation · 2025Article
- In-silico evaluation of putative maternal semiochemicals of pigs with receptor proteins.Frontiers in molecular biosciences · 2025Article
- HDBind: encoding of molecular structure with hyperdimensional binary representations.Scientific reports · 2024Article
- Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA.Molecular biotechnology · 2024Article
- Accelerating COVID-19 Drug Discovery with High-Performance Computing.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations.Nature communications · 2023Article
- PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications.Scientific data · 2022Article
- Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility.Journal of chemical theory and computation · 2022Article
- Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors.Journal of chemical information and modeling · 2022Article
- ReliableJournal of chemical information and modeling · 2022Article
- Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers.Interface focus · 2021Article
- PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.Communications biology · 2021Article
- The effect of protein mutations on drug binding suggests ensuing personalised drug selection.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of molecular simulation to estimate the strength of macromolecular binding free energies is becoming increasingly widespread, with goals ranging from lead optimization and enrichment in drug discovery to personalizing or stratifying treatment regimes. In order to realize the potential of such approaches to predict new results, not merely to explain previous experimental findings, it is necessary that the methods used are reliable and accurate, and that their limitations are thoroughly understood. However, the computational cost of atomistic simulation techniques such as molecular dynamics (MD) has meant that until recently little work has focused on validating and verifying the available free energy methodologies, with the consequence that many of the results published in the literature are not reproducible. Here, we present a detailed analysis of two of the most popular approximate methods for calculating binding free energies from molecular simulations, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA), applied to the nine FDA-approved HIV-1 protease inhibitors. Our results show that the values obtained from replica simulations of the same protease-drug complex, differing only in initially assigned atom velocities, can vary by as much as 10 kcal mol
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Registered trials
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.