The use of methods of deep learning for the virtual screening for COVID19 therapeutic candidates

The DSI is excited to announce funding support to Drs. Artem Cherkasov and Faraz Hach from the Vancouver Prostate Centre for their COVID-19 therapeutic discovery project. This project seeks to use deep learning (DL) techniques to identify potential therapeutic agents to target one of the main proteins (Mpro/3CLpro) that enable the SARS-CoV-2 virus to replicate. A description of the project follows below.

The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structures of several key SARS-CoV-2 proteins have been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates.

The overall goal of this current project is to develop robust virtual screening protocols relying on deep docking algorithms to create custom-scoring functions to be used for ultra-large virtual screening against SARS-CoV-2 3CL Main Protease, among other emerging viral targets. The developed scoring functions will be used in combination with recently emerged Deep Docking protocol capable of processing billions of molecules structures against biological targets of interest. The most attractive features of deep models are that they favour very large and correlated inputs, allows simultaneous optimization for multiple dependent variables, and does not rely on strict features selection (characteristics that are all typical for Big Data, such as Enamile REAL Space database currently used in Cherkasov’s lab and consisting of 13 billion of molecular structures).

In this project, we propose to implement DL-enabled docking scoring functions that will be leveraged by matching structural patterns, ranking chemical structures, generating novel molecular features, etc., to enable the discovery of SARS-CoV-2 Main protease inhibitors with pre-defined properties--including, but not limited to efficacy, favorable pharmacology, synthesizability, patentability, minimized toxicities and off-target effects among others.