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

TL;DR

This paper introduces EISA-score, a novel element interactive surface area scoring method that improves protein-ligand binding affinity prediction by capturing crucial physical and chemical interactions more effectively than traditional surface-area-based methods.

Contribution

The paper presents a new molecular surface representation using element interactive manifolds that enhances scoring accuracy in protein-ligand affinity prediction.

Findings

EISA-score outperforms existing models on PDBbind benchmarks.

Element interactive surface representations improve physical and chemical interaction encoding.

Low-dimensional descriptors enable effective machine learning integration.

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.