Workshop on "High Content Imaging and Data Science for Virtual Screening and Drug Discovery", Bled 2019

High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.