Accelerated discovery and optimization of a potent and selective MMP12 inhibitor with the machine learning platform CAMOLE-1

Machine learning techniques are being readily utilized to accelerate drug discovery pipelines. Peptide therapeutics are an emerging modality that offer the advantage of increased potency with reduced toxicity that are amenable to machine learning discovery and optimization. This work describes the Calico All-Atom Molecule Engine (CAMOLE-1), a machine learning platform for de novo peptide inhibitor design. CAMOLE-1 incorporates an all-atom diffusion model that enables rapid non-canonical peptide discovery and optimization, and integrates physics based computational filtering, and biochemical testing. We applied CAMOLE-1 towards the discovery of peptide inhibitors of matrix metalloproteinases (MMPs), a target class that has essentially evaded therapeutic development. Starting from canonical amino acid-based design, an MMP12 peptide inhibitor was identified and further optimized to improve potency. From these initial peptides, we used our all-atom diffusion model to determine a predicted structure, allowing us to design a potent and selective inhibitor over the course of about one year and 270 synthesized peptides. Predicted structures of both canonical and non-canonical peptide inhibitors showed high fidelity to our experimentally determined peptide-bound crystal structures. To our knowledge, this is the first described effort to use machine learning techniques to iteratively drive peptide inhibitor design beyond initial hit discovery into the development of selective, potent lead peptide inhibitors with improved pharmacokinetic properties. Our study illustrates the power of machine learning-driven design of peptide inhibitors, an exciting emerging therapeutic modality.