COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC50 values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2.

Aims: SARS-CoV-2 which is NovelCoronavirushas been disseminated all over the world and causing Coronavirus disease (COVID-19) resulting in many deaths as well as economic loss in several countries.This virus is showinga considerable amount of high morbidity and mortality.Currently, no drugs are available againstSARS-CoV-2. Therefore,for the treatment of disease, researchers are looking fornew drugs that can treat the disease and prevent it to be spread.In this regard,drug repurposingmay help scientists for treating and preventing infections associated with SARS-CoV-2. Drug repurposingis a strategy that can identify new targets for existing drugs that are already approved for the treatment of a disease.Main methods: In this study, we present a virtual screening procedure employing deep lerning regression method in 9101 drugs from Drug bank database against the target Main protease (Mpro) for the treatment of COVID-19. 500 screened compounds were subjected to docking.Key findings: Among those 500 drugs, 10 best drugs were selected, which had better binding energy as compared to the reference molecule. Based on the Binding energy score, we can suggest that the identified drug may be considered for therapeutic development against the virus.Significance: Drug repurposing has many advantages as it could shorten the time and reduce the cost of new drug discovery. This research will help to get new drugs against COVID-19 and help humans against this pandemic disease. Keyword- Drug Repurposing, Deep learning, Molecular Docking, COVID-19, Drug bank database, MPro

Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol respectively. The drug likeness of the screened compounds was evaluated using FaF-Drug3 server. Finally toxicity of the hit compounds was predicted in cell line using the CLC-Pred server where their cytotoxic ability against various lung cancer cell lines was confirmed. We have shown 4 potential compounds, which could be further exploited as efficient drug candidates against lung cancer.

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