Data access, aggregation, and analysis drive R&D decision-making process. It’s not just about answering a hypothesis; it’s also about determining the next one. The challenge arises in ensuring that the right information is surfaced to the right people at the right time. Relying on disconnected informatics systems, however, has often resulted in critical decisions being made with insufficient information, particularly if that information lives in unstructured data sources such as lab notebooks or conference presentations. The utilization of artificial intelligence (AI) expands the pool of information researchers can leverage either by finding compounds or other entities in unstructured files or predicting their various biophysical and biochemical properties prior to testing in the lab. Looking to the future, AI even has the potential to suggest new compounds for researchers to test based on their previous work.

Attendees of this webinar should expect to learn:

• Why recent advances in Large Language Models are particularly appealing to pharmaceutical R&D
• How AI extracts needed insights and data from unstructured data sources to guide drug candidate selection
• How to accelerate lead identification with AI-driven property prediction
• Best practices for determining how to utilize AI in your organization’s R&D strategy

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