AWS provides a unified environment for multimodal biological foundation models (BioFMs), enabling you to make more confident, timely decision-making in personalized medicine. This AI system combines biological data, model development, scalable compute, and partner tools to support the drug development life cycle. In this post, we’ll explore how multimodal BioFMs work, showcase real-world applications in drug discovery and clinical development, and contextualize how AWS enables organizations to build and deploy multimodal BioFMs.

Biological foundation models (BioFMs) are AI models pre-trained on large biological datasets. BioFMs demonstrate advanced capabilities on specific healthcare and life sciences tasks. The commonly used BioFMs span drug discovery and clinical development domains, particularly in protein structure and molecule design (~20%), omics data analysis including DNA, epigenetic, and RNA (~30%), medical imaging (15%), and clinical documentation (~35%) (Delile et al. 2025).

Unimodal BioFMs are trained exclusively on a single data modality (for example, amino acid sequences) for relevant downstream applications like predicting protein structures; this breakthrough earned the 2024 Nobel Prize in Chemistry. Multimodal BioFMs train across multiple data types (text, audio, image, and video, hereafter “modalities”) and can simultaneously infer across different streams in a single model (for example, text prompts to generate new images or match images to captions).

The current frontier of models pushes the boundary of multimodal understanding and generation capabilities. General-purpose models like Amazon Nova 2 Omni can process text, images, video, and speech inputs while generating both text and images. This multimodality trend extends to BioFMs, where combining multiple data types like medical images and clinical documentation achieves higher predictive accuracy and broader applicability across diverse clinical outcomes (Siam et al. 2025).

These performance gains explain why leading biopharma organizations are increasingly adopting multimodal BioFMs. Leading biopharma organizations invest in BioFMs for analyzing biologic (Merck and Novo Nordisk), genomic (AstraZeneca), pathology (Bayer), and clinical (Roche) data. You can realize up to 50% in cost and time savings for drug development and up to 90% in time savings for medical image diagnosis when using these specialized AI models (State of the Art-ificial Intelligence 2025, Jeong et al. 2025). Multimodal BioFMs show promise in multiple stages of the healthcare and life sciences value chain (Figure 1).