AI-driven drug discovery depends on large-scale, information-rich biological datasets that capture how compounds perturb cellular systems across diverse conditions.
Building these datasets requires scalable approaches that can combine phenotypic and molecular readouts in high-throughput screening workflows.
In this webinar, hosted by DDW, Daniel Alpern, Chief Scientific Officer at Alithea Genomics, and David Sorrell, Director of Cell-Based Screening at Revvity, will discuss how MERCURIUS™ DRUG-seq enables scalable transcriptome-wide profiling for large-scale compound screens. By generating rich molecular signatures directly from perturbation experiments, DRUG-seq provides pathway-level and mechanism-of-action insights that complement phenotypic screening approaches such as cell painting.
Join this webinar to explore how combining transcriptomic and morphological profiling can create richer perturbation datasets for compound characterisation, toxicity assessment, dose-response analysis, and mechanism-of-action studies. Using case studies from screening applications, the session will highlight how integrated phenotypic and transcriptomic workflows can help build the biological data layer needed for next-generation AI-driven drug discovery.
Register for free and watch the webinar on-demand now!
Building these datasets requires scalable approaches that can combine phenotypic and molecular readouts in high-throughput screening workflows.
In this webinar, hosted by DDW, Daniel Alpern, Chief Scientific Officer at Alithea Genomics, and David Sorrell, Director of Cell-Based Screening at Revvity, will discuss how MERCURIUS™ DRUG-seq enables scalable transcriptome-wide profiling for large-scale compound screens. By generating rich molecular signatures directly from perturbation experiments, DRUG-seq provides pathway-level and mechanism-of-action insights that complement phenotypic screening approaches such as cell painting.
Join this webinar to explore how combining transcriptomic and morphological profiling can create richer perturbation datasets for compound characterisation, toxicity assessment, dose-response analysis, and mechanism-of-action studies. Using case studies from screening applications, the session will highlight how integrated phenotypic and transcriptomic workflows can help build the biological data layer needed for next-generation AI-driven drug discovery.Register for free and watch the webinar on-demand now!
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