Abstract Text: Drug discovery pipelines aim to deliver candidate molecules to human clinical trials. Each potential molecule requires strong evidence of biodistribution, biological activity, and lack of toxicity prior to testing in humans. This process is long, expensive, and has a high attrition rate requiring many biological targets to be considered in the early stages of validation in order to ensure a single entry into clinical trial. Integrative omics approaches provide biological and clinical insights for characterizing drug candidates and exploring their functions and connections. However, there is significant heterogeneity in the biological activity of drugs on cells within tissues due to specific differences in the proteome, transcriptome, and metabolome of individual cells. Therefore, single-cell technologies are crucial in providing the necessary resolution to reach a comprehensive understanding of the biological properties underlying human health and disease. Systematic single-cell analyses enable the exploration of heterogeneity within cell populations greatly advancing our understanding of less-defined cell subsets. We present our systematic approach combining high-resolution single cell profiling technologies, state-of-the-art computation and machine learning methodologies, and the latest biological and pharmaceutical findings, to accelerate drug discovery through single-cell forward- and back-translation.
