Effective cocktail treatments for SARS-CoV-2 based on modeling lung single cell response data

To fully model the impact of SARS-CoV-2 requires the integration of several different types of molecular and cellular data. SARS-CoV-2 is known to primarily impact cells via two viral entry factors, ACE2 and TMPRSS2. However, much less is currently known about the virus activity within lung cells. To model host response to viral infection, and to develop potential treatments, we will extend methods based on Continuous State Hidden Markov models (CSHMM) and further combine them with additional graphical models and graph analysis algorithms. Combined, these methods would allow us to reconstruct pathways leading from virus proteins via their host interactions to regulators and finally to the observed expression profiles in each cell. Such models identify not just the optimal individual targets but also combinations of targets that, together, lead to large decrease in viral loads. In parallel we will extend ChemProp, our deep learning method which learns to relate molecules and their associated activity values by developing advanced representations involving optimal transport and molecular prototypes, accumulating evidence about each compound's profile from multiple sources using transfer learning and by extending it to learn the activity of compound mixtures to treat multiple targets. Using our recently developed protocols, we will differentiate human induced pluripotent stem cells to lung cells and perform time series single cell expression studies to profile cells infected with SARS-CoV-2. Following modeling, predicted CSHMM targets, and their predicted ChemProp compounds will be experimentally validated to identify treatments that reduce viral loads in lung cells.