J Chem Inf Model - Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships.

Tópicos

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Resumo

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.

Resumo Limpo

medicin chemistri point view one primari goal high throughput screen hts hit list assess identif chemotyp inform structureact relationship sar chemotyp may enabl optim primari potenc well select phamacokinet properti common way priorit molecular cluster hit typic cluster techniqu howev reli general notion chemic similar standard rule scaffold decomposit thus insensit molecular featur enrich biolog activ compound hinder sar analysi compound share pharmacophor might end cluster thus direct compar medicin chemist similar common chemotyp relat activ may contamin cluster distract import chemic motif combin molecular similar bayesian model introduc robust activityawar cluster approach ii featur map method elucid distinct sar determin polypharmacolog compound evalu method doserespons assay pubchem bioassay repositori activityawar cluster group compound share molecular core specif target pathway hand rather group inact scaffold common found compound seri mani core structur also found literatur discuss sar respect target numer comparison core allow identif structur prerequisit polypharmacolog ie distinct bioactiv region within singl compound point toward selectivityconf medchem strategi method present general applic type activ data may help bridg gap hit list assess design medchem strategi

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