Nonparametric Biologic Design via Discrete Drifting

Recent algorithms for sequence-based therapeutic design use diffusion and flow models as statistical priors for optimization in combinatorial spaces. Adapting these priors to new reference datasets can require additional training. We explore whether a frozen representation encoder and reference data can support such adaptation without fitting a generative model. We introduce **NimblDrift**, a *discrete* feature-space drifting algorithm for generating and optimizing populations of biologic candidates. At each step, we select local edits that reduce a discrepancy between the candidate and reference populations in feature space. On three small-molecule targets, the best-scoring drift configurations exceeded greedy-predictor and random-walk baselines in predicted binding. With **GreedyDrift**, we also improved predicted peptide affinity while retaining reference-like distributions of secondary properties. Allele-specific peptide generation further tests the functional information accessible through representation geometry. For practical design, NimblDrift supports modular search with frozen encoders and replaceable reference populations. For representation research, these experiments offer a direct way to compare encoders through their effects on discrete generation.