Accelerating High Throughput Drug Discovery Using Deep Learning
Image-based high content screening is a well-established method for discovering new compounds at high throughput using their phenotypic signatures. Current processing of these imaging assays relies primarily on tedious and often subjective manual feature extraction requiring specialized skills. We've explored the application of deep convolutional neural networks to address these concerns. Beyond algorithmic hurdles, we faced challenges around building robust production-level models as well as integrating within a pharmaceutical context. This talk will discuss our approach towards building and industrializing deep learning based image analysis workflows within the early phases of drug discovery research.
Yusuf works as a data scientist in drug discovery research at GlaxoSmithKline in Cambridge, MA. Currently, his main focus is on putting together a computer vision platform for early stage drug discovery with broad usability across use cases and imaging domains. He also has previous experience within the areas of compound screening and biomarker identification as well as in building systems that fit a healthcare context. Yusuf was previously employed at Merrimack Pharmaceuticals and received his M.S. from Carnegie Mellon in 2015.