Abstract: Methods and systems for machine learning are disclosed for automated high content screening of drug compounds. Functions in one method include, receiving an assay layout; receiving images of a plurality of wells in one or more plates; training binary AI models based on the positive phenotype controls versus negative control to generate probabilities of an input image being the positive control to which the binary AI models were trained; training an all-control AI model based on all of the positive phenotype controls and the negative control to generate a set of probabilities of an input image being one of the positive phenotype controls or the negative control; and generating one or more visual representations of the set of probabilities to evaluate performance of the trained all-control AI model and the binary AI models.

Abstract: Methods and systems for machine learning are disclosed for early detection of morphological changes in cell condition of biological cells. In one disclosed embodiment, the development of vaccines and anti-virals are sped up using machine learning to identify viral plaques earlier than can be detected using human observation alone. In the disclosed embodiment, detecting morphological changes in virus-infected cells can be made before plaques caused by cell death are observable (typical cell death in 2-14 days). Machine learning brings high-content/high-throughput techniques to the study of virology for the development of novel anti-viral compounds. Machine learning can also be used to characterize the effectiveness of novel anti-viral compounds on rapidly mutating viral strains, such as influenza and SARS-CoV-2.