EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction

Md Masud Rana, Duc Duy Nguyen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.

Original languageEnglish
Pages (from-to)4329-4341
Number of pages13
JournalJournal of Chemical Information and Modeling
Volume62
Issue number18
DOIs
StatePublished - Sep 26 2022

Bibliographical note

Publisher Copyright:
© 2022 American Chemical Society.

Funding

This work was supported in part by NSF Grants DMS-2053284, DMS-2151802, and University of Kentucky Startup Fund.

FundersFunder number
University of Kentucky Startup Fund
National Science Foundation (NSF)DMS-2053284, DMS-2151802

    ASJC Scopus subject areas

    • General Chemistry
    • General Chemical Engineering
    • Computer Science Applications
    • Library and Information Sciences

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