Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Frontiers in Pharmacology

Pharmacology

Ranajit Nath¹  Kamal A. Qureshi²  Bharti Vyas³  Ashok Aspatwar⁴  Seppo Parkkila⁵  Ajay Manaithiya⁶  Ratul Bhowmik⁶ 

[1] Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India;Department of Pharmaceutics, Unaizah College of Pharmacy, Qassim University, Unaizah, Al-Qassim, Saudi Arabia;Department of bioinformatics, School of Interdisciplinary Studies, Jamia Hamdard, New Delhi, India;Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland;Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland;Fimlab Ltd., Tampere University Hospital, Tampere, Finland;Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India;

关键词: molecular docking;    tuberculosis;    drug resistance;    QSAR;    pharmacophore modeling;   

DOI  :  10.3389/fphar.2023.1265573

 received in 2023-07-23, accepted in 2023-08-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

【 授权许可】

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Copyright © 2023 Bhowmik, Manaithiya, Vyas, Nath, Qureshi, Parkkila and Aspatwar.