Open AccessA big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction
Author(s) -
Renjith Paulose,
Kalirajan Jegatheesan,
Gopal Balakrishnan
Publication year - 2018
Publication title -
indian journal of pharmacology/the indian journal of pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 59
eISSN - 1998-3751
pISSN - 0253-7613
DOI - 10.4103/ijp.ijp_304_17
Subject(s) - context (archaeology) , artificial neural network , machine learning , artificial intelligence , computer science , big data , drug discovery , chemical toxicity , virtual screening , data mining , computational biology , chemistry , bioinformatics , biology , toxicity , paleontology , organic chemistry
Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task.