13 Multimodal AI Systems for End-to-End Drug Discovery
This chapter covers:
- How to architect production-ready platforms that integrate diverse AI models.
- Integration patterns used to orchestrate AI components in commercial workflows.
- Deep dives into real-world case studies, including the discovery of rentosertib and billion-scale virtual screening.
- Future directions for biological foundation models, virtual cells, and autonomous systems.
The preceding chapters have equipped you with the fundamental building blocks of computational discovery: molecular representations from cheminformatics, QSAR models for property prediction, structure-based design methods, generative models for de novo molecular design, graph neural networks for drug-target affinity prediction, and transformer architectures for protein structures. However, in a production pharmaceutical environment, these techniques rarely operate in isolation.
The key challenge now is combining these AI techniques into systems that can transform the entire drug discovery pipeline. This chapter synthesizes the techniques and concepts from Chapters 1 through 12 by examining how they orchestrate into multimodal platforms that span the workflow from target identification through clinical candidate selection. Rather than introducing entirely new algorithms, our focus here is understanding the integration patterns and practical considerations for building and deploying these multimodal systems.