title: Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
abstract: The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.
authors.affiliation: Department of Statistics, Strathclyde University, Glasgow, United Kingdom.
authors.forename: R D
authors.initials: RD
authors.lastname: King
authors.forename: S
authors.initials: S
authors.lastname: Muggleton
authors.forename: R A
authors.initials: RA
authors.lastname: Lewis
authors.forename: M J
authors.initials: MJ
authors.lastname: Sternberg
mesh_terms: D001185:Artificial Intelligence; D015195:Drug Design; D004926:Escherichia coli / Q000201:enzymology; D005493:Folic Acid Antagonists; D015394:Molecular Structure; D013329:Structure-Activity Relationship; D014295:Trimethoprim / Q000031:analogs & derivatives* / Q000737:chemistry
chemical_list: D005493:Folic Acid Antagonists; D014295:Trimethoprim