However, current technical limitations for deconvoluting complex protein mixtures and thorough mapping to disease targets have prevented their widespread utilization as a therapeutic treatment. Juvena Therapeutics is overcoming these technical challenges by building a drug development platform that enables systematic investigation of the therapeutic potential of secreted proteins by integrating quantitative proteomics, transcriptomics and machine learning. Candidate proteins are selected from a proprietary disease-modifying protein library derived from human embryonic stem cells using mass spectrometry and transcriptomics. Candidate rank ordering is achieved through machine learning models and bioinformatics leveraged from biological, biophysical, and experimental features. In vitro phenotypic screening and preclinical validation of candidates is accelerated with using deep learning models predicting the cell state, tissue regeneration, and the cognitive behavior of rodents. This platform has identified multiple lead hits across different therapeutic indications. Our goal is to develop tools that enable identification of disease modifying secretomes from other cell types as well as annotating isoform variants and post-translational modifications. Ultimately, Juvena Therapeutics is creating a knowledge graph of regenerative protein biology in which protein ligand-receptor interactions are mapped to a phenotypic response for a given diseased tissue.