Classification of dopamine antagonists using functional feature hypothesis and topological descriptors

• Published in: Bioorganic & medicinal chemistry 2006-03, Vol.14 (5), p.1454-1461
• Main Authors: Kim, Hye-Jung, Cho, Yong Seo, Koh, Hun Yeong, Kong, Jae Yang, No, Kyoung Tai, Pae, Ae Nim
• Format: Article
• Language: English
• Subjects: Algorithms; ANN; BCUT; Binding, Competitive; Biological and medical sciences; Catecholaminergic system; Classification; Databases, Factual; Dopamine - metabolism; Dopamine antagonist; Dopamine Antagonists - chemistry; Dopamine Antagonists - classification; Dopamine Plasma Membrane Transport Proteins - metabolism; Medical sciences; Models, Molecular; Molconn-Z; Neural Networks (Computer); Neuropharmacology; Neurotransmitters. Neurotransmission. Receptors; Pharmacology. Drug treatments; Pharmacophore hypothesis; Quantitative Structure-Activity Relationship; Receptors, Dopamine - metabolism; SIMCA
• ISSN: 0968-0896; 1464-3391
• DOI: 10.1016/j.bmc.2005.09.072

Two-dimensional topological descriptors and three-dimensional pharmacophore hypothesis were used to investigate and compare different classes of dopamine antagonists. Molconn-Z and BCUT topological descriptors were employed to develop a classification model for 1475 dopamine antagonists from MDDR database. The combining both of classification using topological descriptors and functional feature hypotheses could be a useful tool to predict selective antagonists. The designing of selective dopamine antagonists for their own subreceptors can be useful in individual therapy of various neuropsychiatric disorders. Three-dimensional pharmacophore hypothesis and two-dimensional topological descriptors were used to investigate and compare different classes of dopamine antagonists. The structurally diverse D 3 and D 4 antagonists above preclinical trials were selected to map common structural features of highly selective and efficacious antagonists. The generated pharmacophore hypotheses were successfully employed as discriminative probe for database screening. To filter out the false positive from screening hits, the classification models by two-dimensional topological descriptors were built. Molconn-Z and BCUT topological descriptors were employed to develop a classification model for 1328 dopamine antagonists from MDDR database. The soft independent modeling of class analogy and artificial neural network, two supervised classification techniques, successfully classified D 1, D 3, and D 4 antagonists at the average of 80% rates into their own active classes. The mean classification rates for D 2 antagonists were obtained to 60% due to insufficient selective D 2 antagonists. In this paper, we report the validity of our models generated using functional feature hypotheses and topological descriptors. The combining both of classification using functional feature hypotheses and topological descriptors would be a useful tool to predict selective antagonists.