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Journals / Journal of Physical Chemistry and Functional Materials / 2025 / Volume: 8 Issue: 2

Utılızıng Computatıonal Chemıstry To Unravel The Molecular Mechanısms of Cystıc Fıbrosıs Mutatıons And Develop Targeted Therapeutıcs

Muhammad Tukur Ibrahim(Ahmadu Bello University)0000-0002-6146-5667
Muhammad Suleiman Darma(Federal University Dutsin-Ma)0000-0001-8762-1077
Sanı Uba(Ahmadu Bello University)0000-0001-9595-6897
Pages
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Publication Date
2025-12-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings highlight the potential of computational approaches in accelerating drug discovery for cystic fibrosis by identifying and optimizing promising lead compounds.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cystic fibrosis (CF) is a life-shortening genetic disorder caused by mutations in the CFTR gene, leading to dysfunctional chloride ion transport and associated complications.
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Key Findings & Data:Twenty-seven ligand derivatives from 3-(2-benzyloxyphenyl) isoxazoles and isoxazolines previously reported as CFTR activators were analyzed and compared to genistein, a known CF therapeutic.
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Conclusion & Impact:These findings highlight the potential of computational approaches in accelerating drug discovery for cystic fibrosis by identifying and optimizing promising lead compounds.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cystic Fibrosis (CF) · CFTR Mutations · Computational Chemistry

Abstract

Cystic fibrosis (CF) is a life-shortening genetic disorder caused by mutations in the CFTR gene, leading to dysfunctional chloride ion transport and associated complications. This study employs computational chemistry to explore the molecular mechanisms underlying CF and to aid the development of targeted therapeutics. Twenty-seven ligand derivatives from 3-(2-benzyloxyphenyl) isoxazoles and isoxazolines previously reported as CFTR activators were analyzed and compared to genistein, a known CF therapeutic. Ligand structures were optimized using Density Functional Theory (DFT), and molecular descriptors were computed with PaDEL software. Using Genetic Function Approximation (GFA) via Material Studio, Quantitative Structure-Activity Relationship (QSAR) models were developed to correlate molecular features with biological activity (pIC₅₀). The top model (R² = 0.974) demonstrated high predictive power and reliability, validated through cross-validation and applicability domain analysis. Key descriptors such as SpDiam_Dt, GATS4v, and MATS7i significantly influenced model performance, offering insights into molecular traits critical for CF treatment efficacy. These findings highlight the potential of computational approaches in accelerating drug discovery for cystic fibrosis by identifying and optimizing promising lead compounds.

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Cite This Article

Ibrahim, M. T., Darma, M. S., Uba, S. (2025). Utılızıng Computatıonal Chemıstry To Unravel The Molecular Mechanısms of Cystıc Fıbrosıs Mutatıons And Develop Targeted Therapeutıcs. Journal of Physical Chemistry and Functional Materials, Volume: 8 Issue: 2. https://doi.org/10.54565/jphcfum.1721789

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