title: Molecular properties of psychopharmacological drugs determining non-competitive inhibition of 5-HT3A receptors.
abstract: We developed a structure-property-activity relationship (SPAR)-model for psychopharmacological drugs acting as non-competitive 5-HT(3A) receptor antagonists by using a decision-tree learner provided by the RapidMiner machine learning tool. A single molecular descriptor, namely the molecular dipole moment per molecular weight (mu/MW), predicts whether or not a substance non-competitively antagonizes 5-HT-induced Na(+) currents. A low mu/MW is compatible with drug-cumulation in apolar lipid rafts. This study confirms that size-intensive descriptors allow the development of compact SPAR models.
authors.affiliation: Department of Psychiatry and Psychotherapy, University of Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany. johannes.kornhuber@uk-erlangen.de
authors.forename: Johannes
authors.initials: J
authors.lastname: Kornhuber
authors.forename: Lothar
authors.initials: L
authors.lastname: Terfloth
authors.forename: Stefan
authors.initials: S
authors.lastname: Bleich
authors.forename: Jens
authors.initials: J
authors.lastname: Wiltfang
authors.forename: Rainer
authors.initials: R
authors.lastname: Rupprecht
mesh_terms: D001665:Binding Sites; D003198:Computer Simulation; D016208:Databases, Factual; D008956:Models, Chemical; D008958:Models, Molecular; D015394:Molecular Structure; D008970:Molecular Weight; D011619:Psychotropic Drugs / Q000737:chemistry / Q000494:pharmacology*; D058831:Serotonin 5-HT3 Receptor Antagonists; D013329:Structure-Activity Relationship
chemical_list: D011619:Psychotropic Drugs; D058831:Serotonin 5-HT3 Receptor Antagonists