title: Generative models for chemical structures.
abstract: We apply recently developed techniques for pattern recognition to construct a generative model for chemical structure. This approach can be viewed as ligand-based de novo design. We construct a statistical model describing the structural variations present in a set of molecules which may be sampled to generate new structurally similar examples. We prevent the possibility of generating chemically invalid molecules, according to our implicit hydrogen model, by projecting samples onto the nearest chemically valid molecule. By populating the input set with molecules that are active against a target, we show how new molecules may be generated that will likely also be active against the target.
authors.affiliation: Software Technologies Research Group, University of Bamberg, 96047 Bamberg, Germany.
authors.forename: David
authors.initials: D
authors.lastname: White
authors.forename: Richard C
authors.initials: RC
authors.lastname: Wilson
mesh_terms: D020134:Catalytic Domain; D002626:Chemistry, Pharmaceutical; D052246:Cyclooxygenase 2 Inhibitors / Q000737:chemistry; D016503:Drug Delivery Systems; D066246:ErbB Receptors / Q000737:chemistry; D006801:Humans; D008956:Models, Chemical; D015394:Molecular Structure
chemical_list: D052246:Cyclooxygenase 2 Inhibitors; C512478:EGFR protein, human; D066246:ErbB Receptors