A new procedure for improving the predictiveness of CoMFA models and its application to a set of dihydrofolate reductase inhibitors.
A new automated procedure to improve the predictive quality of CoMFA models for both training and test sets is described. A model of greater consistency is generated by performing small reorientations of the underlying molecules for which too low activities are calculated. In order to predict activities of test compounds, the most similar molecules in the previously optimized model are identified and used as a basis for the prediction. This method has been applied to two independent sets of dihydrofolate reductase inhibitors (80 compounds each, serving as training sets), resulting in a significant increase of the cross-validated r2 value. For both models, the predictive r2 value for a test set consisting of 70 compounds was improved substantially.
Kroemer
R T
RT
Sandoz-Forschungsinstitut, Vienna, Austria.
Hecht
P
P
D003198:Computer Simulation; D017076:Computer-Aided Design; D015195:Drug Design; D005493:Folic Acid Antagonists / Q000737:chemistry*; D006801:Humans; D008958:Models, Molecular; D015394:Molecular Structure; D011487:Protein Conformation; D013329:Structure-Activity Relationship; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry*
D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase