Synthesis and Identification of Novel Potential Thiadiazole Based Molecules Containing 1,2,3-triazole Moiety Against COVID-19 Main Protease Through Structure-Guided Virtual Screening Approach

In the present study, 1-(5-Methyl-1-(5-(methylthio)-1,3,4-thiadiazol-2-yl)-1H-1,2,3-triazol-4-yl) ethan-1-one 2 was used as predecessor molecule for synthesis of methyl 2-(1-(5-methyl-1-(5-(methylthio)-1,3,4-thiadiazol-2-yl)-1H-1,2,3-triazol-4-yl) ethylidene) hydrazine-1-carbodithioate 3. The latter compound was utilized to synthesize two new 1,3,4-thiadiazole-1,2,3-triazole hybrids 6 and 7 via its reaction with the appropriate hydrazonoyl halide derivatives 4 and 5. All the newly prepared derivatives' chemical composition was inferred from microanalytical and spectral data (FT-IR, Mass spectrometry, 1H-NMR, and 13C-NMR). Coronavirus disease (COVID-19) is an infectious disease that can cause respiratory tract infections of humans and vertebrate animals. Herein, we reported a structure-aided in silico virtual screening to identify their antiviral activity against Coronavirus through inhibition of main coronavirus protease, which plays an essential role in propagating the disease. The newly synthesized compounds showed good docking scores to COVID-19 main protease.

Discovery of small molecule pathway regulators by image profile matching

Identifying chemical regulators of biological pathways is currently a time-consuming bottleneck in developing therapeutics and small-molecule research tools. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to a disease of interest. Here, we instead use a broad, virtual screening approach that matches compounds to pathways based on phenotypic information in public data. Our computational strategy efficiently uncovered small molecule regulators of three pathways, containing p38ɑ (MAPK14), YAP1, or PPARGC1A (PGC-1α). We first selected genes whose overexpression yielded distinct image-based profiles in the Cell Painting assay, a microscopy assay involving six stains that label eight cellular organelles/components. To identify small molecule regulators of pathways involving those genes, we used publicly available Cell Painting profiles of 30,616 small molecules to identify compounds that yield morphological effects either positively or negatively correlated with image-based profiles for specific genes. Subsequent assays validated compounds that impacted the predicted pathway activities. This image profile-based drug discovery approach could transform both basic research and drug discovery by identifying useful compounds that modify pathways of biological and therapeutic interest, thus using a computational query to replace certain customized labor- and resource-intensive screens.