This research direction focuses on data-driven intelligent drug design and precision diagnosis and therapy, aiming to shift biomedical discovery from experience-driven approaches toward a synergistic data- and mechanism-driven paradigm enabled by artificial intelligence. The primary emphasis is on brain-related disorders, including Alzheimer’s disease, brain tumors, and neurodegenerative conditions, with the goal of uncovering key molecular networks and dynamic regulatory mechanisms underlying disease progression.

By integrating machine learning, graph neural networks, and multimodal medical imaging analysis, the research develops interpretable predictive models for disease progression and therapeutic response. These models enable systematic identification of therapeutic targets, optimization of treatment strategies, and design of personalized precision medicine approaches.

The framework follows the emerging AI for Science paradigm, forming a closed-loop workflow that connects data acquisition, mechanistic learning, predictive modeling, and experimental validation. The overall objective is to advance the integration of brain science and artificial intelligence, enabling next-generation intelligent diagnostics and therapeutics for complex neurological diseases.