Year: 2024
Type: article
Abstract: Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The standard homology theory is based on a differential rule for the boundary operator that satisfies [Formula: see text] = 0. Our recent work has extended this rule by employing Mayer homology with generalized differentials that satisfy [Formula: see text] = 0 for [Formula: see text] 2, leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analyses in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities.
Authors: Hongsong Feng, Li Shen, Jian Liu, Guo-Wei Wei
Institutions: Michigan State University, Chongqing University of Technology
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Foundation for the National Institutes of Health, National Aeronautics and Space Administration, Bristol-Myers Squibb, Pfizer, Michigan State University Foundation, Nuclear Safety and Security Commission, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases
Awards: AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies, G2333693723, G2551507366, G314050322, G4604530488, G4934647071, Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets, G5117234996, Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data, G6065440738, DMS/NIGMS 1: Data-driven Ricci curvatures and spectral graph for machine learning and adaptive virtual screening, III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data, G6227120222, Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug Discovery, G754965114, G9000094880, G9000099788, G9000294604, G945659648, G965062884
OpenAlex ID: W4402689955