Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology

Mitotic checkpoints safeguard genomic integrity by orchestrating the precise segregation of chromosomes during cell division. Yet their complex, nonlinear dynamics have long defied full understanding through traditional experimental and computational approaches. In recent years, artificial intelligence (AI) has begun to transform this landscape. Machine learning and deep learning methods now achieve substantial accuracies in predicting cellular behaviors and uncovering novel regulatory mechanisms within checkpoint networks. Advances include transformer architectures capable of predicting spindle assembly checkpoint engagement with >95% accuracy, graph neural networks that decode kinetochore-microtubule dynamics at subpixel resolution, and hybrid AI-mechanistic models that reveal previously hidden feedback circuits. By integrating multi-omics data and bridging molecular mechanisms with clinical applications, AI-driven approaches are opening significant opportunities for precision medicine in cancer and other proliferative diseases. This review synthesizes emerging computational frameworks, highlights transformative AI-driven discoveries, and proposes a roadmap for developing predictive, personalized models of mitotic checkpoint control-charting a path from computational insight to clinical impact.