ArticleJournal of Taibah University Medical Sciences2023
2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents.
Article in Journal of Taibah University Medical Sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrated QSAR, Molecular Docking, ADMET Profiling, and Antioxidant Evaluation of Substituted Chromone and Aryloxyalkanoic Acid Derivatives as Potential CysLTPharmaceuticals (Basel, Switzerland) · 2026Article
- Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.Mini reviews in medicinal chemistry · 2026Review
- Article
- Rational design of allosteric inhibitors targeting C797S mutant EGFR in NSCLC: an integrative in silico andFrontiers in oncology · 2025Article
- Computer-aided discovery of novel SmDHODH inhibitors for schistosomiasis therapy: Ligand-based drug design, molecular docking, molecular dynamic simulations, drug-likeness, and ADMET studies.PLoS neglected tropical diseases · 2024Article
- Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies.Journal of Taibah University Medical Sciences · 2024Article
- Virtual screening and molecular dynamics studies of novel small molecules targetingIn silico pharmacology · 2024Article
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC. Methods: The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively. Results: The model generated via 2D-QSAR modeling was highly predictive with R Conclusion: After successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.
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Registered trials
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