Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.

ABSTRACT: Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of pr

SUBMITTER: Isert C 

PROVIDER: S-EPMC10835705 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature