Utilizing Embeddings for Drug Discovery in Billion-Scale Databases

Vector embeddings are commonly utilized for tasks such as indexing documents, machine translation, and image searches. Yet in scientific applications, they are often impractical due to their black box nature. In Deep MedChem, we have attempted to adapt semantic search methodologies from NLP to the indexing of chemical formulas. Is it as simple as that? How can we ensure the interpretability and consistency of the generated embeddings? And are current vector databases scalable enough?

Web: https://deepmedchem.com/

CHEESE (molecular search): https://cheese.themama.ai/
Documentation: https://cheese-docs.themama.ai/
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⌚️ Start: 16:00 and end at 17:00, both online and offline in Miton offices, Křížíkova 34, Praha - reception will have the necessary information.
🎙️ Speaker: Miroslav Lžičař
🍻 Networking after the talk.
🎥 Recording: After the event we will publish a recording and post a link to it in the comments.
🚪Doors open at 15:45, and the event officially starts at 16:00.

Your expertise is about to take off. Can't wait to have you on board!