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Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis
Research article (International Journal of Computational Biology and Drug Design, 2018) · cited 14× · AI/ML
Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis
Summary
Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis is a scholarly article[1].
Key Facts
Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis. Retrieved May 24, 2026, from https://4ort.xyz/entity/comparative-analysis-of-machine-learning-based-qsar-models-and-molecular-docking-studies-to-screen-potential-anti-tuberc
MLA“Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/comparative-analysis-of-machine-learning-based-qsar-models-and-molecular-docking-studies-to-screen-potential-anti-tuberc.
BibTeX@misc{4ortxyz_comparative-analysis-of-machine-learning-based-qsar-models-and-molecular-docking-studies-to-screen-potential-anti-tuberc_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis}}, year = {2026}, url = {https://4ort.xyz/entity/comparative-analysis-of-machine-learning-based-qsar-models-and-molecular-docking-studies-to-screen-potential-anti-tuberc}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis — https://4ort.xyz/entity/comparative-analysis-of-machine-learning-based-qsar-models-and-molecular-docking-studies-to-screen-potential-anti-tuberc (retrieved 2026-05-24)
