title: A probabilistic approach to high throughput drug discovery.
abstract: A methodology is presented in which high throughput screening experimental data are used to construct a probabilistic QSAR model which is subsequently used to select building blocks for a virtual combinatorial library. The methodology is based upon statistical probability estimation and not regression. The methodology is applied to the construction of two focused virtual combinatorial libraries: one for cyclic GMP phosphodiesterase type V inhibitors and one for acyl-CoA:cholesterol O-acyltransferase inhibitors. The results suggest that the methodology is capable of selecting combinatorial substituents that lead to active compounds starting with binary (pass/fail) activity measurements.
authors.affiliation: Chemical Computing Group Inc, 1010 Sherbrooke Street West, Suite 910, Montreal, Canada, H3A 2R7. paul@chemcomp.com
authors.forename: Paul
authors.initials: P
authors.lastname: Labute
authors.forename: Shahul
authors.initials: S
authors.lastname: Nilar
authors.forename: Christopher
authors.initials: C
authors.lastname: Williams
mesh_terms: D015106:3',5'-Cyclic-GMP Phosphodiesterases / Q000037:antagonists & inhibitors; D020650:Combinatorial Chemistry Techniques; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000138:chemical synthesis / Q000737:chemistry; D011336:Probability; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D002785:Sterol O-Acyltransferase / Q000037:antagonists & inhibitors
chemical_list: D004791:Enzyme Inhibitors; D002785:Sterol O-Acyltransferase; D015106:3',5'-Cyclic-GMP Phosphodiesterases