Transcriptome-Conditioned Personalized De Novo Drug Generation for AML Using Metaheuristic Assembly and Target-Driven Filtering

Abdullah G. Elafifi; Basma Mamdouh; Mariam Hanafy; Muhammed Alaa Eldin; Yosef Khaled; Nesma Mohamed El-Gelany; Tarek H. M. Abou-El-Enien

arXiv · 2512.21301

Transcriptome-Conditioned Personalized De Novo Drug Generation for AML Using Metaheuristic Assembly and Target-Driven Filtering

Abdullah G. Elafifi·Basma Mamdouh·Mariam Hanafy·Muhammed Alaa Eldin·Yosef Khaled·Nesma Mohamed El-Gelany·Tarek H. M. Abou-El-Enien

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Abstract

Acute Myeloid Leukemia (AML) remains a clinical challenge due to its extreme molecular heterogeneity and high relapse rates. While precision medicine has introduced mutation-specific therapies, many patients still lack effective, personalized options. This paper presents a novel, end-to-end computational framework that bridges the gap between patient-specific transcriptomics and de novo drug discovery. By analyzing bulk RNA sequencing data from the TCGA-LAML cohort, the study utilized Weighted Gene Co-expression Network Analysis (WGCNA) to prioritize 20 high-value biomarkers, including metabolic transporters like HK3 and immune-modulatory receptors such as SIGLEC9. The physical structures of these targets were modeled using AlphaFold3, and druggable hotspots were quantitatively mapped via the DOGSiteScorer engine. Then developed a novel, reaction-first evolutionary metaheuristic algorithm as well as multi-objective optimization programming that assembles novel ligands from fragment libraries, guided by spatial alignment to these identified hotspots. The generative model produced structurally unique chemical entities with a strong bias toward drug-like space, as evidenced by QED scores peaking between 0.5 and 0.7. Validation through ADMET profiling and SwissDock molecular docking identified high-confidence candidates, such as Ligand L1, which achieved a binding free energy of -6.571 kcal/mol against the A08A96 biomarker. These results demonstrate that integrating systems biology with metaheuristic molecular assembly can produce pharmacologically viable, patient tailored leads, offering a scalable blueprint for precision oncology in AML and beyond

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Abdullah G. Elafifi, Basma Mamdouh, Mariam Hanafy, Muhammed Alaa Eldin, Yosef Khaled, Nesma Mohamed El-Gelany, Tarek H. M. Abou-El-Enien. 2025-12-24. Transcriptome-Conditioned Personalized De Novo Drug Generation for AML Using Metaheuristic Assembly and Target-Driven Filtering. https://arxiv.org/abs/2512.21301

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