title: How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.
abstract: Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.
authors.affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, CB2 1EW, Cambridge, United Kingdom.
authors.forename: Alexios
authors.initials: A
authors.lastname: Koutsoukas
authors.forename: Shardul
authors.initials: S
authors.lastname: Paricharak
authors.forename: Warren R J D
authors.initials: WR
authors.lastname: Galloway
authors.forename: David R
authors.initials: DR
authors.lastname: Spring
authors.forename: Adriaan P
authors.initials: AP
authors.lastname: Ijzerman
authors.forename: Robert C
authors.initials: RC
authors.lastname: Glen
authors.forename: David
authors.initials: D
authors.lastname: Marcus
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D062126:Databases, Chemical; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods* / Q000706:statistics & numerical data; D004353:Drug Evaluation, Preclinical; D006801:Humans; D008956:Models, Chemical; D015394:Molecular Structure; D025341:Principal Component Analysis