Landolfi, Laura (2025) Design of Computational Frameworks for Complex Diseases: Drug Discovery Challenges and Epigenetic Mechanisms. [Tesi di dottorato]

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Abstract

This thesis leverages artificial intelligence (AI), machine learning (ML), and molecular dynamics (MD) to address key challenges in drug discovery and disease biology. The research first introduces a novel Pareto-based framework to advance multi-objective optimization in generative AI for drug design (Chapter 1), using metabolic disease targets as case studies. This multi-target concept was then applied to the COVID-19 pandemic by designing dual-acting bile acid derivatives intended to inhibit SARS CoV-2 and modulate host response (Chapter 2). A new computational pipeline for fragment-based drug design was subsequently developed by adapting a cosolvent-MD simulation platform, originally for proteins, to target G-quadruplex DNA structures (Chapter 3). Finally, the thesis shifts to disease mechanism discovery, presenting a bioinformatic study that correlates placental DNA methylation changes with maternal stress and hypertensive disorders during the COVID-19 pandemic (Chapter 4). Collectively, this work demonstrates the power of computational tools in modern biomedicine. By developing and applying novel in silico techniques, this thesis offers original contributions that bridge methodological gaps and provide new perspectives for the rational discovery of therapies and the understanding of complex disease pathways.

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