Post by Ardigen

Building AI capabilities before defining the scientific problem is a common mistake. As multimodal foundation models gain traction in drug discovery, the challenge is no longer just building larger models. It's creating systems that can integrate diverse biological and clinical data into reliable, decision-ready insights. In our latest article, we explore what it takes to move beyond AI pilots and build multimodal systems that can support real R&D programs. Inside, we discuss: → Why multimodal data should be treated as a connected representation of biology - not isolated datasets → When foundation models make sense, and when agentic architectures are a better fit → The role of knowledge graphs, ontologies, and causal reasoning in scientific AI → Five capabilities organizations need before scaling multimodal AI → Why these systems should become long-term discovery infrastructure rather than one-off projects If you're shaping AI, data, or translational strategy in pharma or biotech, this article provides a practical framework for evaluating multimodal AI in drug discovery. Read the blog post: https://lnkd.in/dRs5iW49 #biotech #lifesciences #AI #AIinDrugDiscovery #AIinBiotech #DataDrivenBiotech #KnowledgeGraphs #ArdigenAI