Nanome, Inc., a virtual reality (VR) startup whose signature product is a computational chemistry software platform, has co-authored a paper describing 10 potential small molecule COVID-19 inhibitors targeting the SARS-CoV-2 main protease that were generated by artificial intelligence (AI). The study was conducted in collaboration with Insilico Medicine, an artificial intelligence company based in Hong Kong, and could reveal as-of-yet-undiscovered methods for attacking the virus that have eluded scientists working with existing drug candidates.
Many potential therapeutics aimed at containing the spread of SARS-CoV-2 have targeted the S, or spike, protein, a surface protein that plays a vital role in viral entry into host cells. However, according to the authors, two-thirds of the SARS-CoV-2 genome comprises non-structural proteins, such as the viral protease (the protein necessary for viral replication), which shouldn’t be overlooked as potential therapeutic targets.
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“The SARS-CoV-2 main protease is a much more druggable protein than the spike protein,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine and lead author on the paper. “It contains a pocket perfect for small molecule inhibitors.”
But since the beginning of the COVID-19 outbreak, only a few studies on novel SARS-CoV-2 protease inhibitors have been published.
“One reason for this is the daunting number of chemical structures that can be generated from scratch,” said Zhavoronkov. “Consequently, conventional computational drug design approaches tend to include a limited number of fragments and/or employ sophisticated search strategies to sample hit compounds from a predefined area of the chemical space.”
To enable scientists to exploit the whole drug-like chemical space, scientists have developed a new type of computational method for drug discovery using recent advances in deep learning and AI. Zhavoronkov calls it “AI imagination”.
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Insilico’s proprietary AI imagination platform has already been successfully applied to design small molecule drugs for a wide range of human diseases, such as cancer, fibrosis, and immunological diseases.
In the study published to ResearchGate (May 11, 2020) and chemrxiv.org (May 19, 2020), the authors used a protein structure published to the Protein Data Bank (PDB) website by Purdue University to generate a number of novel, non-covalent drug candidates.
The generation was followed by COVID-19 selection of 10 representative examples and medicinal chemistry analysis in VR, provided by Nanome.