Unsupervised Representation Learning for Proteochemometric Modeling.

ABSTRACT: In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein-ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of relate

SUBMITTER: Kim PT 

PROVIDER: S-EPMC8657702 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature