Multimodal Foundation Models and Quantum-Accelerated AI for Drug Discovery and Precision Healthcare: Survey and Framework

The rapid expansion of heterogeneous biomedical data—including molecular graphs, protein conformations, genomic sequences, medical imaging, and clinical records—has intensified the demand for artificial intelligence (AI) frameworks capable of unified and biologically grounded analysis. Although recent deep learning advances have achieved notable success within individual biomedical domains, most existing approaches remain unimodal or weakly multimodal, resulting in fragmented representations and limited generalization across complex biological systems. In drug discovery, generative models such as variational autoencoders, generative adversarial networks, and diffusion-based approaches show promise in molecular design but often lack explicit protein structure conditioning, limiting biochemical validity and target specificity. Graph neural networks support molecular representation learning but face scalability challenges due to increasing architectural complexity. Quantum machine learning has been proposed to enhance molecular property prediction, yet its application remains largely theoretical. In clinical settings, predictive models predominantly rely on correlational learning, reducing interpretability and cross-institutional robustness. This paper presents a comprehensive survey and unified problem formulation highlighting the need for multimodal biomedical foundation models integrating generative chemistry, quantum–classical hybrid learning, and causal inference to advance next-generation drug discovery and precision healthcare.