C2PO: an ML-powered optimizer of the membrane permeability of cyclic peptides through chemical modification.

Peptide drug development is currently receiving due attention as a modality between small and large molecules. Therapeutic peptides represent an opportunity to achieve high potency, selectivity, and reach intracellular targets. A new era in the development of therapeutic peptides emerged with the arrival of cyclic peptides which avoid the limitations of parenteral administration via achieving sufficient oral bioavailability. However, improving the membrane permeability of cyclic peptides remains one of the principal bottlenecks. Here, we introduce a deep learning regression model of cyclic peptide membrane permeability based on publicly available data. The model starts with a chemical structure and goes beyond the limited vocabulary language models to generalize to monomers beyond the ones in the training dataset. Moreover, we introduce an efficient estimator2generative wrapper to enable using the model in direct molecular optimization of membrane permeability via chemical modification. We name our application C2PO (Cyclic Peptide Permeability Optimizer). Lastly, we demonstrate how a molecule correction tool can be used to limit the presence of unfamiliar chemistry in the generated molecules.Scientific contribution: We provide an ML-driven optimizer application, named C2PO, that returns structurally modified cyclic peptides with an improved membrane permeability, one of the pivotal tasks in drug discovery and development. C2PO is a first-in-class application for cyclic peptide permeability amelioration, in that it converts a ML model into a generative optimizer of chemical structures. Additionally, through demonstration we incentivize the usage of an automated post-correction tool with a chemistry reference library to correct strange chemistry outputs from C2PO, a known issue for ML-generated chemical structures.