Andre Watson

Biography
Andre Watson’s work focuses on AI-driven biomolecular design to transform targeting as applied to therapeutic delivery. He develops predictive peptides and receptor-targeting ligands engineered to direct nanoparticles, gene-editing systems, drugs, and nucleic acids to specific cells and tissues. By combining machine learning with physics-based modeling, his work aims to increase therapeutic specificity, reduce systemic toxicity, and enable precise delivery of next-generation medicines. Central to this effort is the development of foundation models for organ- and tissue-specific targeting. These models are trained on large-scale structural, biophysical, and receptor-expression data to learn the governing principles of receptor–ligand interactions. At inference time, the system performs zero-shot peptide generation, designing novel targeting ligands de novo using only a receptor’s structure or sequence as input. The model does not rely on prior interaction datasets or known ligands at inference, enabling rapid generation of peptides that selectively bind receptors associated with specific organs, tissues, or disease-relevant cell types. Watson’s current platform builds on more than a decade of research in gene-delivery nanomaterials and targeted therapeutic systems. While conducting research at Rensselaer Polytechnic Institute, he developed delivery technologies for guided nucleases such as CRISPR gene editing and TALEN gene editing, focusing on enabling targeted delivery of gene-editing tools to defined cell populations. That work evolved into a broader effort to engineer programmable delivery systems capable of directing diverse therapeutic modalities—including mRNA, gene editing, biologics, and small molecules—to precise biological targets. By integrating receptor discovery, generative peptide design, and predictive binding thermodynamics into a single computational platform, Watson’s work establishes a programmable targeting layer for modern medicine. With more than 80 global patents across targeting systems, delivery technologies, and AI-driven ligand design, he and his team aim to make tissue-specific delivery a programmable capability for therapeutics.