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The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput X-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (Mpro), which is essential for viral replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to Mpro. In subsequent cell-based viral reduction assays, one peptidomimetic and six non-peptidic compounds showed antiviral activity at non-toxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2.

Ever since the first rational approaches to the discovery of promising lead candidate structures were applied, it has been a challenge for both medicinal and computational chemists to assess, generate, and combine promising structural motifs to form new and potent chemical entities for biological screening against potential drug targets. Many scientists have committed themselves to the analysis and identification of valuable chemical building blocks and have also developed strategies on how to best recombine them. In this context, the retrosynthetic fragmentation and recombination of chemical motifs derived from known inhibitors is a common and well-known procedure. Meanwhile, fragment-based approaches have become established and valuable processes in pharmaceutical lead discovery and validation. Several application studies have yielded promising lead candidates. [2] Chemical space is huge. Corporate as well as public databases are in the millions and are still increasing in size in order to cover a larger part of the chemical universe. For several good reasons, there is the common trend to standardize experimental and computational protocols in pharmaceutical research. This trend demands systematic and consistent approaches, although they can hardly match the creativity and intuition of medicinal chemists. Consequently, they can and should not substitute, but rather assist, the expert in this task. The most prominent automated example for fragment generation is the retrosynthetic combinatorial analysis procedure (RECAP).[3] It was the first of its kind to apply 11 distinct rules that were supposed to model chemical motifs that could easily be formed by combinatorial chemistry. In this context, the "fragment space" concept was introduced. In contrast to a fragment library, such a space consists not only of a set of fragments, but also of a set of rules that specifies how to recombine fragments by fusing the respective chemical motifs.RECAP is widely used and often referred to, yet even though authors frequently state to have used modified improved versions of the original, actual publications that communicate the extensions that were carried out are rare. An extension of the fragment space concept was recently published, but with a focus on obtaining scaffolds and not on retaining supposedly 'drug-like' substituents or functional groups.[4] Apart from that, the question remains what a 'drug-like' fragment space actually is, and whether or not 'drug-likeness' depends on the origin of the fragments: that is, if they necessarily have to be derived from drugs. In this context, it is highly interesting and important to measure the extent and accuracy with which current models and methods are able to represent the available chemical space.In an attempt to improve existing approaches for the automatic decomposition of molecules into fragments, we compiled a new and more elaborate set of rules for the breaking of retrosynthetically interesting chemical substructures (BRICS) and used this for obtaining fragments from biol...

An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.

The glucose-sensing enzyme glucokinase (GK) plays a key role in glucose metabolism. We report here the effects of a novel glucokinase activator, LY2121260. The activator enhanced GK activity via binding to the allosteric site located in the hinge region of the enzyme. LY2121260 stimulated insulin secretion in a glucose-dependent manner in pancreatic beta-cells and increased glucose use in rat hepatocytes. In addition, incubation of beta-cells with the GK activator resulted in increased GK protein levels, suggesting that enhanced insulin secretion on chronic treatment with a GK activator may be due to not only changed enzyme kinetics but also elevated enzyme levels. Animals treated with LY2121260 showed an improved glucose tolerance after oral glucose challenge. These results support the concept that GK activators represent a new class of compounds that increase both insulin secretion and hepatic glucose use and in doing so may prove to be effective agents for the control of blood glucose levels in patients with type 2 diabetes.

To identify possible candidates for progression towards clinical studies against SARS-CoV-2, we screened a well-defined collection of 5632 compounds including 3488 compounds which have undergone clinical investigations (marketed drugs, phases 1 -3, and withdrawn) across 600 indications. Compounds were screened for their inhibition of viral induced cytotoxicity using the human epithelial colorectal adenocarcinoma cell line Caco-2 and a SARS-CoV-2 isolate. The primary screen of 5632 compounds gave 271 hits. A total of 64 compounds with IC50 <20 µM were identified, including 19 compounds with IC50 < 1 µM. Of this confirmed hit population, 90% have not yet been previously reported as active against SARS-CoV-2 in-vitro cell assays. Some 37 of the actives are launched drugs, 19 are in phases 1-3 and 10 pre-clinical. Several inhibitors were associated with modulation of host pathways including kinase signaling P53 activation, ubiquitin pathways and PDE activity modulation, with long chain acyl transferases were effective viral inhibitors.

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro and have identified 62 additional compounds with IC50 values below 1 μM and profiled their selectivity toward chymotrypsin and 3CL-Pro from the Middle East respiratory syndrome virus. A subset of eight inhibitors showed anticytopathic effect in a Vero-E6 cell line, and the compounds thioguanosine and MG-132 were analyzed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 Å, showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity.

Several descriptors applied to peptide structure−activity and/or structure−property relationships have been developed in recent years. This report describes new descriptors for the natural amino acids which have been derived from the principal component analysis (PCA) applied on the MS-WHIM 3D-description matrices. MS-WHIM indexes are a collection of 36 statistical indexes aimed at extracting and condensing steric and electrostatic 3D-properties of a molecule. These new descriptors have been developed both on extended side-chain conformation and on rotamer library of natural amino acids. The method appeared to be more potent when describing a single conformation (i.e. extended) than when applied collectively on library conformation families. MS-WHIM scores, however, were shown to efficacely describe and correctly classify the natural amino acid features and to provide sound statistical models either predicting activities of two peptide sets taken as a test or correlating amino acid chemicophysical properties, like water-accessible surface area, to general structural features of amino acids.