Biophysics & Data-Driven Modeling

This line aims to understand biological systems at molecular resolution and to develop predictive tools for biomedical research. It integrates high-performance computing with molecular dynamics, structural bioinformatics and machine learning, including deep learning approaches, to study biomolecular structure, dynamics and function. Applications include the analysis of protein conformational changes, pathogenic mechanisms, biomolecular recognition and in silico drug design. It also enables benchmarking emerging quantum and quantum-inspired computational methods against classical approaches in realistic biomedical problems, including comparisons between classical machine-learning models and quantum machine-learning strategies for predicting the pathogenicity of genetic mutations. It connects computational biophysics, AI-assisted molecular medicine, and the methodological frontier of quantum-enabled data analysis.