Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

• Published in: PLoS computational biology Vol. 14; no. 1; p. e1005929
• Main Authors: Cang, Zixuan, Mu, Lin, Wei, Guo-Wei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.01.2018; Public Library of Science (PLoS)
• Subjects: Accuracy; Algebra; Algorithms; Area Under Curve; Artificial intelligence; Artificial neural networks; Binding; Biochemistry & Molecular Biology; Bioinformatics; Biological research; Biology; Biology and Life Sciences; Biomolecules; Computational biology; Computational Biology - methods; Computational chemistry; Computer and Information Sciences; Coordination compounds; Databases, Protein; Datasets; Electrostatic properties; Electrostatics; Funding; Homology; Humans; Induction algorithms; Learning algorithms; Ligands; Machine Learning; Mathematical & Computational Biology; Mathematics; Methods; Models, Neurological; Molecular Dynamics Simulation; Molecular interactions; Neural networks; Neural Networks, Computer; Nucleic Acids - chemistry; Physical Sciences; Physics; Predictions; Protein Binding; Protein Interaction Mapping; Proteins; Proteins - chemistry; Research and Analysis Methods; Screening; Static Electricity; Topology; United States > US; East Lansing Michigan; Michigan

This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the co...