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With Johannes Freudenberg (Dir. Computational Biology, GSK), Michael White (Research Advisor, Pfizer), Yolanda Chong (VP, Recursion Pharma), Michael Keiser (Prof., University of California - San Francisco), Nicholas Tatonetti (Prof. Biomedical Informatics, Columbia).
Thu, Jun 04, 2020 @ 05:00 PM   Not Known   Online
 
     
 
 
              

      
 
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Phenotypic screening has recently led to discoveries of first-in-class drugs with novel mechanisms of action. Due to this success, interest in this strategy has experienced a renaissance. In contrast to target-based strategies, phenotypic drug discovery does not rely on knowledge of a specific drug target or a hypothesis about its role in disease, but instead focuses on finding a gene or small molecule that corrects a cellular characteristic that is specific to disease. Along with this new strategy, major advances in high throughput technologies have resulted in immense phenotypic datasets. However, these recent advances in automated screening technology have resulted in a dilemma: experimental data can be generated at a much faster rate than researchers can possibly analyze & integrate them. This inefficiency makes it difficult to conduct certain types of experiments on a large scale, wastes investments, & delays the drug discovery process. This symposium will provide a snapshot of the current state of computational methods used in phenotypic screening & novel in silico approaches, & include discussions of deep learning, AI, functional genomics, chemical screening, systems biology, target deconvolution, biomarkers, & toxicity.
 
 
 
 
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