Yixiang Mao

Noncanonical amino acids push peptide design spaces to the 10⁵⁰ scale, where exhaustive screening is infeasible. PepOpt encodes each residue as a position-specific molecular fingerprint, predicts potency with linear regression, and uses dynamic programming under an additivity assumption to find the globally optimal sequence deterministically. Across 11 internal peptide series it matches or beats nonlinear models, and in 10²⁰ and 10⁵⁰ spaces it returns higher predicted scores than a genetic algorithm.

Noncanonical amino acids push peptide design spaces to the 10⁵⁰ scale, where exhaustive screening is infeasible. PepOpt encodes each residue as a position-specific molecular fingerprint, predicts potency with linear regression, and uses dynamic programming under an additivity assumption to find the globally optimal sequence deterministically. Across 11 internal peptide series it matches or beats nonlinear models, and in 10²⁰ and 10⁵⁰ spaces it returns higher predicted scores than a genetic algorithm.