Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as Mt InhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit Mt InhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R 2 )= 0.8789, Cross-validation leave one out correlation coefficient (Q 2 LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q 2 LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCC tr )=0.9355, CCC ext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their Mt InhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the Mt InhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and Mt InhA protein that may be necessary to develop new lead compounds against tuberculosis.

Authors

Institutions

Publication Details

Journal
Chemical Physics Impact
Published
2024-11-03
DOI
https://doi.org/10.1016/j.chphi.2024.100769
Citations
21
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
7.59
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Kalirajan Rajagopal, Trupti Sameer Chitre, Kalyani Dhirendra Asgaonkar, Krishna S. Shevate et al.
21 citations
Chemical Physics Impact
Computational Drug Discovery Methods
7.59
article

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Kalirajan Rajagopal, Trupti Sameer Chitre, Kalyani Dhirendra Asgaonkar, Krishna S. Shevate, Aniket L. Bhatambrekar, Venkatesan Saravanan, D. Priya, Muthukumaradoss Kathiravan, Neha M. Mhetre
article en
21 citations

Abstract

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as Mt InhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit Mt InhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R 2 )= 0.8789, Cross-validation leave one out correlation coefficient (Q 2 LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q 2 LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCC tr )=0.9355, CCC ext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their Mt InhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the Mt InhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and Mt InhA protein that may be necessary to develop new lead compounds against tuberculosis.

Chemical Physics ImpactVol. 9
JSS Academy of Higher Education and Research (IN), SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 2%
Computational Drug Discovery Methods
7.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.