title: Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery.
abstract: Medicinal chemists' "intuition" is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists' decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ~4,000 available fragments. Based on each chemist's selections, computational classifiers were built to model each chemist's selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1-2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.
authors.affiliation: Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
authors.forename: Peter S
authors.initials: PS
authors.lastname: Kutchukian
authors.forename: Nadya Y
authors.initials: NY
authors.lastname: Vasilyeva
authors.forename: Jordan
authors.initials: J
authors.lastname: Xu
authors.forename: Mika K
authors.initials: MK
authors.lastname: Lindvall
authors.forename: Michael P
authors.initials: MP
authors.lastname: Dillon
authors.forename: Meir
authors.initials: M
authors.lastname: Glick
authors.forename: John D
authors.initials: JD
authors.lastname: Coley
authors.forename: Natasja
authors.initials: N
authors.lastname: Brooijmans
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D002626:Chemistry, Pharmaceutical; D003657:Decision Making; D055808:Drug Discovery; D006801:Humans; D004364:Pharmaceutical Preparations / Q000032:analysis*; D057566:Self Report
chemical_list: D004364:Pharmaceutical Preparations