Authors
Ross D King, Stephen Muggleton, Richard A Lewis, MJ Sternberg
Publication date
1992/12/1
Journal
Proceedings of the national academy of sciences
Volume
89
Issue
23
Pages
11322-11326
Description
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.
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Scholar articles
RD King, S Muggleton, RA Lewis, MJ Sternberg - Proceedings of the national academy of sciences, 1992