Evidence map›Paper›PMID 39656550›Full record

ArticleJournal of chemical information and modeling2024

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.

Negin Forouzesh, Fatemeh Ghafouri, Igor S Tolokh, Alexey V Onufriev

Abstract read
In one paragraph

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. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Negin ForouzeshDepartment of Computer Science, California State University, Los Angeles, California 90032, United States.ORCID 0000-0003-2293-0391
Fatemeh GhafouriGenetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States.
Igor S TolokhDepartment of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States.
Alexey V OnufrievDepartment of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States.ORCID 0000-0002-4930-6612

Funding

Next generation implicit solvation for atomistic modelingR01GM144596 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI ONUFRIEV, ALEXEY VLAD · 2022 to 2025
$1.2M
Improving the Accuracy of Implicit Solvents with a Physics-Guided Neural NetworkR16GM146633 · NIGMS · CALIFORNIA STATE UNIVERSITY LOS ANGELES · PI FOROUZESH, NEGIN · 2022 to 2025
$755k
NIGMS NIH HHS R01 GM144596NIGMS NIH HHS R16 GM146633
6 · The paper itself

Abstract

Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of

Indexed as

SolventsThermodynamicsModels, MolecularProtein BindingProteinsWaterProteinsSolventsWater

Identifiers

PMID39656550
PMCPMC11684022

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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.