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Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data
Author(s) -
Azzaoui Kamal,
Hamon Jacques,
Faller Bernard,
Whitebread Steven,
Jacoby Edgar,
Bender Andreas,
Jenkins Jeremy L.,
Urban Laszlo
Publication year - 2007
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.200700036
Subject(s) - promiscuity , safety pharmacology , computational biology , pharmacology , profiling (computer programming) , drug , drug development , bayes' theorem , drug discovery , computer science , chemistry , data mining , medicine , bioinformatics , biology , artificial intelligence , bayesian probability , ecology , operating system
This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high‐throughput screening data or for lead optimization.