The new LLM allows the client’s research team to explore molecular properties and relationships more effectively.
Meet our client
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Client’s Challenge
The client sought to leverage their knowledge base of in-vivo experiments to boost their in-silico molecule discovery efforts. They needed a solution that could integrate their existing data to accelerate the discovery of new molecules.
Our Solution
Working with the client’s in-silico drug discovery team, we developed a pipeline to train multi-modal LLMs with a deep understanding of chemistry. We integrated Llama 3.1 and Graphormer, incorporating molecular structures as graphs into the LLM, creating a unique model that could analyze textual, SMILES, and molecular data.
Client’s Benefits
The new LLM allows the client’s research team to explore molecular properties and relationships more effectively. It can also predict the properties of new molecules generated through chemical reactions, even those it has never seen before, significantly enhancing in-silico drug discovery capabilities.
