Revolutionizing drug discovery: Integrating artificial intelligence with quantitative systems pharmacology

• The integration of AI and LLMs is enhancing the generation, interpretability, and reproducibility of QSP models. • Generative AI is accelerating drug discovery and development by designing novel molecules and creating digital twins for personalized therapy simulations. • Retrieval-augmented generation architectures are improving QSP simulations by enabling real-time evidence retrieval from vast datasets. • The concept of QSPaaS is emerging, promising to democratize access to powerful modeling and simulation tools. • Explainability, regulatory acceptance, and data integration remain key challenges to the widespread adoption of AI in QSP. Quantitative systems pharmacology (QSP) provides a mechanistic framework for integrating diverse biological, physiological, and pharmacological data to predict drug interactions and clinical outcomes. Recent advances in artificial intelligence (AI) might transform QSP by enhancing model generation, parameter estimation, and predictive capabilities. AI-driven databases and cloud-based platforms might support QSP model development and facilitate QSP as a service (QSPaaS). However, challenges such as computational complexity, high dimensionality, explainability, data integration, and regulatory acceptance persist. This review critically evaluates the integration of AI within QSP, highlighting novel methodologies like surrogate modeling, virtual patient generation, and digital twin technologies. It also discusses current limitations and outlines strategies for future integration to enhance precision medicine, regulatory acceptability, and mechanistic interpretability in drug discovery and development.