Journal of Computer-Aided Molecular Design

, Volume 5, Issue 6, pp 617–628

A machine learning approach to computer-aided molecular design

  • Giorgio Bolis
  • Luigi Di Pace
  • Filippo Fabrocini
Research Papers

DOI: 10.1007/BF00135318

Cite this article as:
Bolis, G., Di Pace, L. & Fabrocini, F. J Computer-Aided Mol Des (1991) 5: 617. doi:10.1007/BF00135318

Summary

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

Key words

Artificial Intelligence Structure-activity relationship Thermolysin inhibitors 

Copyright information

© ESCOM Science Publishers B.V. 1991

Authors and Affiliations

  • Giorgio Bolis
    • 1
  • Luigi Di Pace
    • 2
  • Filippo Fabrocini
    • 2
  1. 1.Farmitalia Carlo Erba srl, Erbamont GroupR&D/CAMDMilanItaly
  2. 2.Artificial Intelligence GroupIBM Rome Scientific CenterRomeItaly

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