Zhavoronkov, Alex Aladinskiy, Vladimir Zhebrak, Alexander Zagribelnyy, Bogdan Terentiev, Victor Bezrukov, Dmitry S. Polykovskiy, Daniil Shayakhmetov, Rim Filimonov, Andrey Orekhov, Philipp Yan, Yilin Popova, Olga Vanhaelen, Quentin Aliper, Alex Ivanenkov, Yan Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches <div> <div> <p>The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at <a href="http://www.insilico.com/ncov-sprint/">www.insilico.com/ncov-sprint/</a>. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. <br></p> </div> </div> generative chemistry;2019 nCoV;3C-like Protease;hypothesis;COVID2019;COVID-2019 2020-02-19