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On May 15, we hosted the AWS Life Sciences Executive Symposium in Boston, where I led our track on ‘accelerating pharma drug discovery with Machine Learning (ML)’. Over 300 life sciences executives from across 100 organizations attended this half-day, in-person event to explore how they can drive innovation through robust data foundations and machine learning on the cloud.
The opening sessions of the symposium showcased how AWS is enabling life sciences organizations leverage generative AI, FMs (Foundation Models), and LLMs (Large Language Models) for drug discovery, across use cases like identifying potential adverse drug reaction, and searching clinical trial datasets. We announced easy-to-run protein algorithms for a range of open-source molecular models and demonstrated how AWS can significantly reduce the cost and time needed to generate usable protein structures. Attendees were excited about the ability to access Ready2Run workflows within the Amazon Omics services for single API or console deployment of AlphaFold and ESMFold algorithms with transparent , run-based pricing and scalable infrastructure. We also previewed AWS’ Drug Discovery Workbench, based on the AWS Batch Architecture for Protein Folding and Design, that supports multiple algorithms within a shared user interface including DiffDock, RFDesign, RFDiffusion, ProteinMPNN, AlphaFold, OpenFold, OmegaFold, and ESMFold.