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 language | English |
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Pages (from-to) | 4329-4341 |
Number of pages | 13 |
Journal | Journal of Chemical Information and Modeling |
Volume | 62 |
Issue number | 18 |
DOIs | |
State | Published - 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.
Funders | Funder number |
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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