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 reduc...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings of the National Academy of Sciences - PNAS Ročník 89; číslo 23; s. 11322
Hlavní autori: King, R D, Muggleton, S, Lewis, R A, Sternberg, M J
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.12.1992
Predmet:
Artificial Intelligence
Drug Design
Escherichia coli - enzymology
Folic Acid Antagonists
Molecular Structure
Structure-Activity Relationship
Trimethoprim - analogs & derivatives
Trimethoprim - chemistry
ISSN:0027-8424
On-line prístup:Zistit podrobnosti o prístupe
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0027-8424
DOI:10.1073/pnas.89.23.11322