Screening of benzenesulfonamide in combination with chemically diverse fragments against carbonic anhydrase by differential scanning fluorimetry.

The differential scanning fluorimetry (DSF) screening of 5.692 fragments in combination with benzenesulfonamide (BSA) against bovine carbonic anhydrase (bCA) delivered >100 hits that either caused, on their own, a significant thermal shift (ΔTm, °C) in the protein melting temperature or significantly influenced the thermal shift observed for BSA alone. Three hits based on 1,2,3-triazole moiety represent the periphery of the recently reported potent inhibitors of hCA II, IX and XII which were efficacious in vivo. Such a re-discovery of suitable BSA periphery essentially validates the new fragment-based approach to the discovery of future CAIs. Structures of other validated fragment hits are reported.

Further validation of strecker-type α-aminonitriles as a new class of potent human carbonic anhydrase II inhibitors: hit expansion within the public domain using differential scanning fluorimetry leads to chemotype refinement.

Testing of an expanded, 800-compound set of analogues of the earlier described Strecker-type α-aminonitriles (selected from publicly available Enamine Ltd. Screening Collection) in thermal shift assay against bovine carbonic anhydrase (bCA) led to further validation of this new class of inhibitors and identification a new, refined chemotype represented by inhibitors with 10-improved potency. [Formula: see text].

SAR by Space: Enriching Hit Sets from the Chemical Space.

We introduce SAR-by-Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolecular hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand- and structure-based approaches in hit generation and lead optimization stages.

Pros and cons of virtual screening based on public "Big Data": In silico mining for new bromodomain inhibitors.

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). Nevertheless, 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.