title: Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors.
abstract: We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.
authors.affiliation: Department of Organic Chemistry, Institute of Chemistry, University of Silesia, PL-40-006 Katowice Poland. polanski@us.edu.pl
authors.forename: Jaroslaw
authors.initials: J
authors.lastname: Polanski
authors.forename: Andrzej
authors.initials: A
authors.lastname: Bak
authors.forename: Rafal
authors.initials: R
authors.lastname: Gieleciak
authors.forename: Tomasz
authors.initials: T
authors.lastname: Magdziarz
mesh_terms: D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry*; D005493:Folic Acid Antagonists / Q000737:chemistry*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D013762:Tetrahydrofolate Dehydrogenase / Q000378:metabolism*
chemical_list: D004791:Enzyme Inhibitors; D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase