Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR

Citation
N. Greene et al., Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR, SAR QSAR EN, 10(2-3), 1999, pp. 299
Citations number
26
Categorie Soggetti
Chemistry
Journal title
SAR AND QSAR IN ENVIRONMENTAL RESEARCH
ISSN journal
1062936X → ACNP
Volume
10
Issue
2-3
Year of publication
1999
Database
ISI
SICI code
1062-936X(1999)10:2-3<299:KESFTA>2.0.ZU;2-3
Abstract
It has long been recognised that the ability to predict the metabolic fate of a chemical substance and the potential toxicity of either the parent com pound or its metabolites are important in novel drug design. The popularity of using computer models as an aid in this area has grown considerably in recent years. LHASA Limited has been developing knowledge-based expert systems for toxici ty and metabolism prediction in collaboration with industry and regulatory authorities. These systems, DEREK, StAR and METEOR, use rules to describe t he relationship between chemical structure and either toxicity in the case of DEREK and StAR, or metabolic fate in the case of METEOR. The rule refinement process for DEREK often involves assessing the predicti ons for a novel set of compounds and comparing them to their biological ass ay results as a measure of the system's performance. For example, 266 non-c ongeneric chemicals from the National Toxicology Program database have been processed through the DEREK mutagenicity knowledge base and the prediction s compared to their Salmonella typhimurium mutagenicity data. Initially, 81 of 114 mutagens (71%) and 117 of 152 non-mutagens (77%) were correctly ide ntified. Following further knowledge base development, the number of correc tly identified mutagens has increased to 96 (84%). Further work on improvin g the predictive capabilities of DEREK, StAR and METEOR is in progress.