chapter thirteen
13 Multimodal AI Systems for End-to-End Drug Discovery
This chapter covers
- Orchestrating diverse AI models into unified, end-to-end discovery workflows.
- Architectural patterns for connecting predictive and generative components.
- Implementing closed-loop active learning cycles to refine model performance.
- Real-world case studies spanning target discovery to clinical trials.
- Critical assessment of clinical outcomes, challenges, and future trends.
Having established the fundamental building blocks of computational discovery in preceding chapters, we now turn to the "fourth wave" of innovation: integrated multimodal AI systems. This chapter examines how production-ready platforms orchestrate diverse models—spanning genomics, protein structure, and small molecule design—into cohesive, end-to-end workflows that accelerate the journey from target identification to clinical trials. We will explore how these systems leverage closed-loop feedback to continuously refine their predictions based on experimental data, transforming isolated tools into autonomous engines capable of navigating the complex, multi-objective landscape of pharmaceutical research.