title: Computational prediction and validation of an expert's evaluation of chemical probes.
abstract: In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.
authors.affiliation: Collaborative Drug Discovery, Inc. , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States.
authors.forename: Nadia K
authors.initials: NK
authors.lastname: Litterman
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Lipinski
authors.forename: Barry A
authors.initials: BA
authors.lastname: Bunin
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D015335:Molecular Probes / Q000737:chemistry* / Q000191:economics; D008970:Molecular Weight; D011786:Quality Control; D012680:Sensitivity and Specificity
chemical_list: D015335:Molecular Probes