title: Comparison between neural networks (NN) and principal component analysis (PCA): structure activity relationships of 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues).
abstract: The applicability of the neural network computer package PSDD (Perceptron Simulator for Drug Design/Perceptron-type Neural Network Simulator) in structure-activity relationship (SAR) studies was investigated. A group of 1,4-dihydropyridine derivatives was used in order to compare the PSDD results with those obtained previously with PCA. Calculated atomic and molecular descriptors using the semiempirical AM1 method were mainly used. It was shown that the predictive capability demonstrated by PSDD in SAR analysis were almost equivalent to that of PCA.
authors.affiliation: Instituto de Química, Universidade Estadual de Campinas, CP 6154, Campinas, São Paulo, 13083-970, Brazil. taka@iqm.unicamp.br
authors.forename: Yuji
authors.initials: Y
authors.lastname: Takahata
authors.forename: Maria Cristina Andreazza
authors.initials: MC
authors.lastname: Costa
authors.forename: Anderson Coser
authors.initials: AC
authors.lastname: Gaudio
mesh_terms: D000465:Algorithms; D002121:Calcium Channel Blockers / Q000737:chemistry* / Q000494:pharmacology*; D016208:Databases, Factual; D004095:Dihydropyridines / Q000737:chemistry* / Q000494:pharmacology*; D005544:Forecasting; D020128:Inhibitory Concentration 50; D016571:Neural Networks, Computer; D009543:Nifedipine / Q000031:analogs & derivatives* / Q000494:pharmacology; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
chemical_list: D002121:Calcium Channel Blockers; D004095:Dihydropyridines; C038806:1,4-dihydropyridine; D009543:Nifedipine