2D-QSAR and molecular docking study on nitrofuran analogues as antitubercular agents

• DOI: 10.3934/molsci.2024001
• Publication Date: 2024-01-04T03:09:06Z
• Authors: Smriti Sharma; Brij K. Sharma; Surabhi Jain; Anubhav Rana
• Keywords: Morpholine; Thiazole

<abstract><sec> <title>Background</title> <p>Resistance to most of the antitubercular drugs has been on rising trends due misuse existing drugs. This encouraged us explore a novel scaffold that potential for quick antimicrobial action with minimum side effects. Nitrofurans have attracted their extensive biological activities, such as antibacterial and antifungal activities.</p> </sec><sec> <title>Objective</title> <p>The activities 126 nitrofuran derivatives investigated by using indicator parameters topological structural fragment descriptors.</p> <title>Methods</title> different quantitative structure activity relationship (QSAR) models created validated two methodologies: combinatorial protocol in multiple linear regression (CP-MLR) partial least-squares (PLS) analysis.</p> <title>Results</title> 16 descriptors identified CP-MLR are from six classes: Constitutional, Functional, Atom Centered Fragments, Topological, Galvez, 2D autocorrelation. Indicator Dragon suggested presence furan ring substituted nitro group is essential activity. Further constitutional, functional classes suggest number double bonds, sulphur atoms fragments like thiazole, morpholine thiophene should be minimum, along positive influence Kier-Hall electrotopological states (Ss) improved The ACF class descriptors, GALVEZ 2D-AUTO descriptor GATS4p also shown TOPO T(O…S) suggests gap between oxygen favorable activity.</p> <title>Conclusions</title> acknowledged study explained variance 72 76% training set prediction test compounds. Also, compounds <bold>122</bold>, <bold>123</bold> <bold>82</bold> were found possess good binding affinity towards nitroreductase.</p> </sec></abstract>