A Physics-Driven Eigenvalue Framework for Selective and Mutation-Resilient Anticancer Drug Design
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| Format: | Recurso digital |
| Language: | English |
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2025
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| _version_ | 1866902328113627136 |
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| author | Rezapour, Majid Rezapour, Ramin |
| author_facet | Rezapour, Majid Rezapour, Ramin |
| contents | <p>This work introduces a physics-driven eigenvalue framework for anticancer drug design, moving beyond traditional geometry-based heuristics and data-driven docking. Instead of treating protein–ligand binding as a rigid lock-and-key or induced-fit process, we model the protein binding pocket as a field-response kernel and ligands as field-energy distributions. Binding affinity and resilience are defined through the dominant eigenvalue (λ_max) and corresponding eigenpartner eigenfunction (f_max) of this kernel.</p> <p>Our framework addresses three major challenges in oncology drug discovery:</p> <p>1. Isoform Selectivity – achieving discrimination between pathological and physiological isoforms (e.g., CA IX vs. CA II).</p> <p>2. Mutation Resilience – retaining activity against resistance mutations such as BCR-ABL T315I, where conventional inhibitors fail.</p> <p>3. Microenvironmental Adaptation – maintaining binding under hypoxic, acidic, and dynamically changing tumor conditions.</p> <p>Simulation and experimental validation show ~30% improvement in isoform selectivity, 85% resilience across 100 mutation variants, and robust efficacy under hypoxia and acidity. In vivo xenografts demonstrate superior tumor reduction (60% vs. 40% for imatinib).</p> <p>Beyond oncology, the eigenpartner principle generalizes to Alzheimer’s disease, HIV protease inhibition, and antibiotic resistance, bridging physics, mathematics, and pharmacology into a single, interpretable, and future-proof drug design paradigm.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17167787 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Physics-Driven Eigenvalue Framework for Selective and Mutation-Resilient Anticancer Drug Design Rezapour, Majid Rezapour, Ramin Anticancer drug design Eigenvalue optimization Field-energy complementarity Mutation resilience Isoform selectivity Quantum-inspired algorithms TDDFT / QM/MM / MD ADMET profiling Precision medicine Physics-based pharmacology <p>This work introduces a physics-driven eigenvalue framework for anticancer drug design, moving beyond traditional geometry-based heuristics and data-driven docking. Instead of treating protein–ligand binding as a rigid lock-and-key or induced-fit process, we model the protein binding pocket as a field-response kernel and ligands as field-energy distributions. Binding affinity and resilience are defined through the dominant eigenvalue (λ_max) and corresponding eigenpartner eigenfunction (f_max) of this kernel.</p> <p>Our framework addresses three major challenges in oncology drug discovery:</p> <p>1. Isoform Selectivity – achieving discrimination between pathological and physiological isoforms (e.g., CA IX vs. CA II).</p> <p>2. Mutation Resilience – retaining activity against resistance mutations such as BCR-ABL T315I, where conventional inhibitors fail.</p> <p>3. Microenvironmental Adaptation – maintaining binding under hypoxic, acidic, and dynamically changing tumor conditions.</p> <p>Simulation and experimental validation show ~30% improvement in isoform selectivity, 85% resilience across 100 mutation variants, and robust efficacy under hypoxia and acidity. In vivo xenografts demonstrate superior tumor reduction (60% vs. 40% for imatinib).</p> <p>Beyond oncology, the eigenpartner principle generalizes to Alzheimer’s disease, HIV protease inhibition, and antibiotic resistance, bridging physics, mathematics, and pharmacology into a single, interpretable, and future-proof drug design paradigm.</p> |
| title | A Physics-Driven Eigenvalue Framework for Selective and Mutation-Resilient Anticancer Drug Design |
| topic | Anticancer drug design Eigenvalue optimization Field-energy complementarity Mutation resilience Isoform selectivity Quantum-inspired algorithms TDDFT / QM/MM / MD ADMET profiling Precision medicine Physics-based pharmacology |
| url | https://doi.org/10.5281/zenodo.17167787 |