Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
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.
King
R D
RD
Department of Statistics, Strathclyde University, Glasgow, United Kingdom.
Muggleton
S
S
Lewis
R A
RA
Sternberg
M J
MJ
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
D005493:Folic Acid Antagonists; D014295:Trimethoprim