Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Abstract

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro protease inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

Ligand-based quantitative structural assessments of SARS-CoV-2 3CLpro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences

“…Our group has already performed several extensive studies on the previous SARS-CoV inhibitors [ 3 , [18] , [19] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ]. In this study, we have reported the structural analysis of 69 diverse SARS-CoV-2 3CL pro inhibitors by the regression-based quantitative structure-activity relationship (QSAR) methodologies to identify the fundamental structural features having crucial effects on SARS-CoV-2 3CL pro inhibition.…”

Generating structural alerts from toxicology datasets using the local interpretable model-agnostic explanations method

“…3,4 In fact, this tool combined with machine learning has a range of applications in many areas of science such as pharmacology, [5][6][7][8][9][10] genetics and biochemistry, [11][12][13][14][15] and drug discovery for COVID-19. 16 However, the model explainability in machine learning is a highly essential issue [17][18][19][20][21] because machine learning models are mostly considered as black boxes, 17,[21][22][23][24] indicating an ambitious challenge to the progress of machine learning.…”

High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors

“…Generally, designed drugs for COVID-19 treatment can be classified into four groups including drugs that prevent the replication and synthesis of RNA by targeting critical enzymes for the replication of the virus, drugs that block the binding of spike protein to ACE2 receptor on human cells, drugs that inhibit coronavirus virulence factors and drugs that inhibit a receptor or enzymes in human cells [ 24 ]. 3C-like cysteine protease (3CL pro ) is the main protease of SARS-CoV-2 that catalyzes the cleavage of polypeptides to their effector forms and has essential enzymatic role for virus life cycle [ 25 , 26 ]. So it can be considered as a target for design drugs in COVID-19 treatment [ 27 – 29 ].…”