ArticleJournal of computer-aided molecular design2020
Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Computation of Protein-Ligand Binding Free Energies with a Quantum Mechanics-Based Mining Minima Algorithm.Journal of chemical theory and computation · 2025Article
- A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CBJournal of molecular modeling · 2024Article
- Molecular Dynamics and Other HPC Simulations for Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA.Journal of computer-aided molecular design · 2022Article
- Recent Developments in Free Energy Calculations for Drug Discovery.Frontiers in molecular biosciences · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF
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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.