ArticleJournal of clinical tuberculosis and other mycobacterial diseases2020
Quantitative structure-activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents.
Article in Journal of clinical tuberculosis and other mycobacterial diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Recent advances in Xanthone-based molecular hybrids: emerging therapeutic strategies for multifactorial diseases.Molecular diversity · 2026Review
- Crescent-Shaped Xanthone-Benzimidazole Conjugates as New Target Probes for Telomeric G‑Quadruplex DNA and Specific Cytotoxicity on Cancer Cells.ACS pharmacology & translational science · 2025Article
- Exploration of pyrazole-based pyridine-4-carbohydrazide derivatives as drug-resistantFuture medicinal chemistry · 2025Article
- Importance of Computer-aided Drug Design in Modern Pharmaceutical Research.Current drug discovery technologies · 2025Review
- Evaluation of the Usefulness of Topological Indices for Predicting Selected Physicochemical Properties of Bioactive Substances with Anti-Androgenic and Hypouricemic Activity.Molecules (Basel, Switzerland) · 2023Article
- 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 · 2023Review
- Advances in computational frameworks in the fight against TB: The way forward.Frontiers in pharmacology · 2023Review
- QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones againstAntibiotics (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationship (QSAR) and molecular docking approach were carried out to design novel anti-tuberculosis agents based on xanthone derivatives. QSAR designed new compounds were calculated by Austin Model 1 (AM1) methods and analysis of multi-linear regression (MLR). The result showed that the best model as follows: Log IC50 = 3.113 + 11.627 qC1 + 15.955 qC4 + 11.702 qC9, this result has appropriate some statistical parameters (PRESS = 2.11, r2 = 0.730, SEE = 0. 3545, R = 0.6827, FCal/FTab = 4.68), and being used to design a potential anti-tuberculosis drugs with substituted amide, sulfoxide, and carboxylate group xanthone scaffold by a number of their inhibitory concentration (IC50). The mechanism action of sulfonamide substituted on the xanthone scaffold as anti-tuberculosis was carried out using molecular docking. Docking inhibition studies were carried out on MTB C171Q receptor (4C6X.pdb) as KasA inhibitors using by the discovery studio. Based on the binding interaction showed, the sulfonamide substituted xanthone has potential being the anti-tuberculosis drugs by KasA inhibitor for target drug activity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.