GV20 Therapeutics have computationally extracted hundreds of millions of tumor-infiltrating antibody sequences from tumor RNA-seq profiles. Using AI trained on these tumor-infiltrating antibodies, GV20 can de novo design antibodies against targets without any known antibody sequences against the targets. This approach not only designs antibodies enriched in functional binders and good developability profiles, but also provides insights on target identification and validation.

In cancer immunotherapy, AI, ML, and DL are critical in developing precision-based targeted therapeutics and personalized molecular engineering. The complex cell signaling pathways, protein-to-protein interactions (PPI), cancer hallmark expressions, and the integral relationship of the differential multi-omic data expression necessitate a high confidence ranking, mapping, modeling, and selection architecture in developing small molecules, peptides, and peptide oligonucleotide conjugates. Cancer immunoediting is a molecular control process requiring a robust multi-omic design pipeline. The key features to consider in creating a design are immunoediting in cancer (elimination, equilibrium, escape) and mechanisms of action (immunosurveillance, immune induction, regulation, augmentation, signal editing, and adaptation.)