title: Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.
abstract: One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR)--the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives--has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
authors: Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, U.K.
authors: J D
authors: JD
authors: Hirst
authors: R D
authors: RD
authors: King
authors: M J
authors: MJ
authors: Sternberg
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005493:Folic Acid Antagonists; D066298:In Vitro Techniques; D016014:Linear Models; D008128:Logic; D051379:Mice; D008956:Models, Chemical; D016571:Neural Networks, Computer; D051381:Rats; D013329:Structure-Activity Relationship; D014227:Triazines / Q000737:chemistry / Q000494:pharmacology
chemical_list: D005493:Folic Acid Antagonists; D014227:Triazines