Open AccessA Bayesian Target Predictor Method based on Molecular Pairing Energies estimation
Author(s) -
Antoni Oliver,
Vincent Canals,
Josep L. Rosselló
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep43738
Subject(s) - virtual screening , computer science , data mining , bayesian probability , pairing , classifier (uml) , posterior probability , context (archaeology) , similarity (geometry) , preprocessor , artificial intelligence , machine learning , database , drug discovery , bioinformatics , physics , paleontology , superconductivity , quantum mechanics , image (mathematics) , biology
Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule’s pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target.