A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction | Zendy

Renjith Paulose | Zendy; Jegatheesan Kalirajan | Zendy; Gopal Samy Balakrishnan | Zendy

Open Access

A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction

Author(s) -

Renjith Paulose,

Jegatheesan Kalirajan,

Gopal Samy Balakrishnan

Publication year - 2018

Publication title -

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.

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