ESP: A method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases

Gilles Klopman, Suman K. Chakravarti, Hao Zhu, Julian M. Ivanov, Roustem D. Saiakhov

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.

Original languageEnglish (US)
Pages (from-to)704-715
Number of pages12
JournalJournal of Chemical Information and Computer Sciences
Volume44
Issue number2
DOIs
StatePublished - 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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