Daniel has more than 18-years of experience in the biopharma industry, working across multiple functional disciplines, including phenotypic assay development, high-throughput screening, and software engineering. Before Strateos, he was a Research Investigator at Novartis (GNF) and led multiple drug discovery campaigns. His research focused on developing novel phenotypic assays across a wide range of indications. Daniel received a B.S. in Biochemistry from the University of California at Davis and a Ph.D. in Cell and Molecular Biology from the Massachusetts Institute of Technology (MIT).
Reaping the Benefits of an AI-Driven Automated Drug Discovery Platform
Strateos’ roboticized cloud labs shorten its clients' hit identification, H2L and lead optimization cycle times by automating in vitro cell-based/biochemical assays and design and synthesis of small molecules. Ro5 offers AI-driven solutions for target identification, hit identification, H2L, lead optimization and clinical trial analytics. Strateos and Ro5 have built a closed loop, automated design-make-test-analyze (DMTA) system that takes advantage of Strateos’s Cloud Lab Automation-as-a-Service and Ro5’s Knowledge Graph and AI Chemistry platforms. It enables fast progression from target identification to lead optimization and allows scientists to evaluate targets, efficiently identify hit compounds, and rapidly design promising drug candidates. This system has been used to identify a prospective oncology target and initiate a drug discovery program. We show that our automated AI-driven DMTA workflow can rapidly identify the majority of hits with only 10% library screened and a diverse set of scaffolds with only 1% of the library screened for subsequent lead optimization.
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