title: Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships.
abstract: From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.
authors.affiliation: Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, Massachusetts 02139, USA. eugen.lounkine@novartis.com
authors.forename: Eugen
authors.initials: E
authors.lastname: Lounkine
authors.forename: Florian
authors.initials: F
authors.lastname: Nigsch
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
authors.forename: Meir
authors.initials: M
authors.lastname: Glick
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D015195:Drug Design; D057166:High-Throughput Screening Assays / Q000379:methods; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010600:Pharmacology; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations