Abstract: The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging as a promising solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug repurposing strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinity. We shortlisted six favorable drugs and examined their physiochemical and pharmacokinetic properties of these top-ranked selected drugs and their best binding interaction for specific target protease 3CLpro. Our study provides an efficient framework for drug repurposing against COVID-19, and establishes the potential of combining molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates. Our findings contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. Keywords: COVID-19; main protease 3CL; drug repurposing; QSAR model; binding affinity; molecular docking 1 Introduction The COVID-19 outbreak has presented an unprecedented worldwide health emergency, with over 687 million confirmed cases and over 6.8 million deaths globally as of May 2023 according to https://www.worldometers.info/coronavirus/. At present, there is no certain drug available to treat COVID-19, and the development of effective cures has become a priority for researchers globally [1]. COVID-19 is triggered by SARS-CoV-2, a positive-sense single-stranded RNA virus that mainly infects the respiratory tract of humans [2]. When the spike protein attaches to the ACE2 receptor on the surface of human cells, the virus enters the cell, and then it utilizes the host's cellular machinery to replicate and spread throughout the body. Figure 1 depicts the life cycle of a coronavirus.

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages.

Updated An oral medication designed by scientists with the help of AI algorithms could one day treat patients with COVID-19 and other types of diseases caused by coronaviruses. Insilico Medicine, a biotech startup based in New York, announced on Tuesday it had nominated a drug candidate for preclinical trials – the stage before you start testing it on humans. Today's mRNA vaccines boost the body's immunity to COVID-19 by aiding the generation of antibodies capable of blocking the virus's spike protein, stopping the bio-nasty from infecting cells. The small molecule developed by Insilico, however, is used to treat people already infected, and works by preventing the coronavirus from replicating. The preclinical candidate has a specialized structure to target the 3C-like (3CL) protease, an enzyme involved in the viral reproduction of the SARS-CoV-2 coronavirus, Feng Ren, Insilico's chief scientific officer, explained.

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here we propose a novel framework, named AIMEE, integrating AI Model and Enzymology Experiments, to identify inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value less than 3 {\mu}M. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and expanding the boundaries of drug discovery.

BEGIN ARTICLE PREVIEW: By Jessica Kent October 29, 2020 – A deep learning tool can offer more information about SARS-CoV-2 proteins to accelerate COVID-19 drug discovery, according to a study published in Chemical Science. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media. Researchers from Michigan State University (MSU) Foundation repurposed deep learning models to focus on a specific SARS-CoV-2 protein called its main protease. The main protease is a cog in the virus’s protein machinery that’s critical to how the pathogen makes copies of itself. Drugs that disable the main protease could stop the virus from replicating. Dig Deeper The main protease is distinct from all known human proteases, which isn’t always the case. Drugs that attack the viral protease are therefore less likely to disrupt people’s natural biochemistry. The SARS-CoV-2 main protease is also almost identic

In a new study in China, a deep learning model detected COVID19 caused pneumonia from CT scans with comparable performance to expert radiologists. CT is the preferred imaging method for evaluating lung infection, assessing progression, and determining treatment options for patients with pneumonia caused by COVID19. In this study, radiologists used AI to help them evaluate the progression of disease, and with the assistance of this model, radiologists' read time decreased by 65%. The model achieved a per-patient sensitivity of 100% and accuracy of 95.24%. This AI could help improve the efficiency of evaluation and diagnosis especially if the number of people with the virus increases. This article is a preprint and has not been peer-reviewed. This paper reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice. Harvard Medical School students have created a COVID19 curriculum. It includes information about epidemiology, clinical management, testing, treatment, vaccine development, and communication. Each section was reviewed by at least two Harvard Medical School faculty experts. Many modules reference supplemental resources that may be worth accessing in the future and to find the most current statistics of the pandemic. Also included are one-page summaries of each module's key takeaways. COVID19 testing in South Korea is free and convenient and over 250,000 people have already been tested. The South Korean data is valuable because they are testing people who have symptoms and people who have no symptoms. This is unusual because most countries are only testing people who are sick to confirm that they have the virus. South Korea is testing everyone, including asymptomatic people, as a public health measure so that anyone who has the virus can isolate even if they don't feel sick. In most countries asymptomatic people are not tested for COVID19. For example Italy is only testing symptomatic people, whereas South Korea tests everyone and picks up more mild cases.

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.