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Predicting Drug Metabolism ±An Evaluation of the Expert System METEOR

Tipo de material: TextoTextoSeries ; Chemistry & BioDiversity, 2(7), p.872-885, 2005Trabajos contenidos:
  • Testa, B
  • Balmat, A.L
  • Long, A
  • Judson, P
Recursos en línea: Resumen: The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between -local× and -global× systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative)predictions. The predictions for four representative substrates are presented in detail (Figs. 1 ± 4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.
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The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between -local× and -global× systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative)predictions. The predictions for four representative substrates are presented in detail (Figs. 1 ± 4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.

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