From Mechanistic Models to AI Synergy: A Roadmap for Predictive and Personalized Biology

Two paradigms now drive the life sciences. One is the explanatory depth of mathematical modeling, the other the predictive power of artificial intelligence. For decades, mechanistic models have offered a rigorous, hypothesis-driven framework for dissecting biological processes, while AI has proven itself at finding patterns in the large, high-dimensional datasets of modern biology. Each has delivered major advances on its own. Together they promise a synthesis that could redefine the boundaries of biological inquiry. In this review we propose a three-part taxonomy of integration strategies, namely AI-accelerated mechanistic modeling, mechanism-informed AI, and hybrid models spanning one-directional sequential coupling and bidirectional iterative systems, and we survey their applications in systems and synthetic biology, pharmacology and drug discovery, epidemiology, and neuroscience. We then examine seven challenges that stand in the way: interpretability, data sparsity, causality, identifiability, computational scale, multimodal integration, and validation and generalization, and we close with a view of biological digital twins, causal discovery engines, and automated science. Our argument is that combining these approaches is the principal task for a new generation of interdisciplinary scientists who aim to build a predictive and mechanistic science of life.