Why Life Sciences AI Is a Search Problem (Part 2 of 5)

In the second part of a five-part series on the future of AI in life sciences, Harini Gopalakrishnan discusses the shift in focus from building larger AI models to smarter search and retrieval methods, particularly in pharmaceutical research and development. During a panel discussion with leaders from Novo Nordisk, Alkermes, and Harvard Medical School, the conversation highlighted how drug discovery is increasingly viewed as a search problem due to the vast combinatorial space of molecules and proteins. By using tensor embeddings to represent complex relationships between chemical, biological, and textual data, researchers can significantly narrow down the search space, making the drug discovery process more efficient and explainable. This approach contrasts with traditional knowledge graphs by allowing dynamic relationship inference and real-time graph construction, thus enhancing the retrieval and predictive capabilities of AI models in life sciences.