A machine learning approach to computer-aided molecular design.
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.
Bolis
G
G
Farmitalia Carlo Erba srl, Erbamont Group, R&D/CAMD, Milan, Italy.
Di Pace
L
L
Fabrocini
F
F
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D004151:Dipeptides / Q000138:chemical synthesis* / Q000494:pharmacology; D015195:Drug Design; D007700:Kinetics; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000138:chemical synthesis* / Q000494:pharmacology; D011487:Protein Conformation; D012984:Software; D013329:Structure-Activity Relationship; D013820:Thermolysin / Q000037:antagonists & inhibitors*
D004151:Dipeptides; D009842:Oligopeptides; D013820:Thermolysin