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Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors.
J Mol Struct. 2021 Jan 15; 1224:129026.JM

Abstract

As the world struggles against current global pandemic of novel coronavirus disease (COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum antiviral agents. Thus, there is a need of strong and sustainable global collaborative works especially in terms of new and existing data analysis and sharing which will join the dots of knowledge gap. Our present chemical-informatics based data analysis approach is an attempt of application of previous activity data of SARS-CoV main protease (Mpro) inhibitors to accelerate the search of present SARS-CoV-2 Mpro inhibitors. The study design was composed of three major aspects: (1) classification QSAR based data mining of diverse SARS-CoV Mpro inhibitors, (2) identification of favourable and/or unfavourable molecular features/fingerprints/substructures regulating the Mpro inhibitory properties, (3) data mining based prediction to validate recently reported virtual hits from natural origin against SARS-CoV-2 Mpro enzyme. Our Structural and physico-chemical interpretation (SPCI) analysis suggested that heterocyclic nucleus like diazole, furan and pyridine have clear positive contribution while, thiophen, thiazole and pyrimidine may exhibit negative contribution to the SARS-CoV Mpro inhibition. Several Monte Carlo optimization based QSAR models were developed and the best model was used for screening of some natural product hits from recent publications. The resulted active molecules were analysed further from the aspects of fragment analysis. This approach set a stage for fragment exploration and QSAR based screening of active molecules against putative SARS-CoV-2 Mpro enzyme. We believe the future in vitro and in vivo studies would provide more perspectives for anti-SARS-CoV-2 agents.

Authors+Show Affiliations

Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, Madhya Pradesh, 470003, India.Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, P. O. Box 17020, Kolkata, 700032, India.Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, Madhya Pradesh, 470003, India.Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, P. O. Box 17020, Kolkata, 700032, India.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32834115

Citation

Ghosh, Kalyan, et al. "Chemical-informatics Approach to COVID-19 Drug Discovery: Exploration of Important Fragments and Data Mining Based Prediction of some Hits From Natural Origins as Main Protease (Mpro) Inhibitors." Journal of Molecular Structure, vol. 1224, 2021, p. 129026.
Ghosh K, Amin SA, Gayen S, et al. Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors. J Mol Struct. 2021;1224:129026.
Ghosh, K., Amin, S. A., Gayen, S., & Jha, T. (2021). Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors. Journal of Molecular Structure, 1224, 129026. https://doi.org/10.1016/j.molstruc.2020.129026
Ghosh K, et al. Chemical-informatics Approach to COVID-19 Drug Discovery: Exploration of Important Fragments and Data Mining Based Prediction of some Hits From Natural Origins as Main Protease (Mpro) Inhibitors. J Mol Struct. 2021 Jan 15;1224:129026. PubMed PMID: 32834115.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors. AU - Ghosh,Kalyan, AU - Amin,Sk Abdul, AU - Gayen,Shovanlal, AU - Jha,Tarun, Y1 - 2020/08/05/ PY - 2020/06/09/received PY - 2020/07/09/revised PY - 2020/08/04/accepted PY - 2020/8/25/entrez PY - 2020/8/25/pubmed PY - 2020/8/25/medline KW - COVID-19 KW - Monte Carlo based optimization KW - Natural product KW - SARS-CoV Mpro KW - SARS-CoV-2 KW - SPCI analysis SP - 129026 EP - 129026 JF - Journal of molecular structure JO - J Mol Struct VL - 1224 N2 - As the world struggles against current global pandemic of novel coronavirus disease (COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum antiviral agents. Thus, there is a need of strong and sustainable global collaborative works especially in terms of new and existing data analysis and sharing which will join the dots of knowledge gap. Our present chemical-informatics based data analysis approach is an attempt of application of previous activity data of SARS-CoV main protease (Mpro) inhibitors to accelerate the search of present SARS-CoV-2 Mpro inhibitors. The study design was composed of three major aspects: (1) classification QSAR based data mining of diverse SARS-CoV Mpro inhibitors, (2) identification of favourable and/or unfavourable molecular features/fingerprints/substructures regulating the Mpro inhibitory properties, (3) data mining based prediction to validate recently reported virtual hits from natural origin against SARS-CoV-2 Mpro enzyme. Our Structural and physico-chemical interpretation (SPCI) analysis suggested that heterocyclic nucleus like diazole, furan and pyridine have clear positive contribution while, thiophen, thiazole and pyrimidine may exhibit negative contribution to the SARS-CoV Mpro inhibition. Several Monte Carlo optimization based QSAR models were developed and the best model was used for screening of some natural product hits from recent publications. The resulted active molecules were analysed further from the aspects of fragment analysis. This approach set a stage for fragment exploration and QSAR based screening of active molecules against putative SARS-CoV-2 Mpro enzyme. We believe the future in vitro and in vivo studies would provide more perspectives for anti-SARS-CoV-2 agents. SN - 0022-2860 UR - https://www.unboundmedicine.com/medline/citation/32834115/Chemical_informatics_approach_to_COVID_19_drug_discovery:_Exploration_of_important_fragments_and_data_mining_based_prediction_of_some_hits_from_natural_origins_as_main_protease__Mpro__inhibitors_ L2 - http://www.cairn.info/revue-recherche-en-soins-infirmiers-2016-2-page-6.html DB - PRIME DP - Unbound Medicine ER -
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