An approach to the parallel synthesis of hydantoin libraries by reaction of in situ generated 2,2,2-trifluoroethylcarbamates and α-amino esters was developed. To demonstrate utility of the method, a library of 1158 hydantoins designed according to the lead-likeness criteria (MW 200-350, cLogP 1-3) was prepared. The success rate of the method was analyzed as a function of physicochemical parameters of the products, and it was found that the method can be considered as a tool for lead-oriented synthesis. A hydantoin-bearing submicromolar primary hit acting as an Aurora kinase A inhibitor was discovered with a combination of rational design, parallel synthesis using the procedures developed, in silico and in vitro screenings.
Multigram synthesis
of (chlorosulfonyl)benzenesulfonyl fluorides
is described. Selective modification of these building blocks at the
sulfonyl chloride function under parallel synthesis conditions is
achieved. It is shown that the reaction scope includes the use of
(hetero)aromatic and electron-poor aliphatic amines (e.g., amino nitriles).
Utility of the method is demonstrated by preparation of the sulfonyl
fluoride library for potential use as covalent fragments, which is
demonstrated by a combination of in silico and in vitro screening against trypsin as a model enzyme. As
a result, several inhibitors were identified with activity on par
with that of the known inhibitor.
The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure-activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). In spite of this, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.
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