A machine learning approach to computer-aided molecular design

✍ Scribed by Giorgio Bolis; Luigi Pace; Filippo Fabrocini

• Publisher: Springer Netherlands
• Year: 1991
• Tongue: English
• Weight: 715 KB
• Volume: 5
• Category: Article
• ISSN: 0920-654X
• DOI: 10.1007/bf00135318

✦ Synopsis

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The arUficial 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 interacUon 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 actlve/inacuve 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 physica~ and chemical properties is utihzed during the inducUve process.