A decoupled alignment kernel for peptide membrane permeability predictions

Cyclic peptides are promising modalities for targeting intracellular sites; however, cell-membrane permeability remains a key bottleneck, exacerbated by limited public data and the need for well-calibrated uncertainty. Instead of relying on data-eager complex deep learning architecture, we propose a monomer-aware decoupled global alignment kernel (MD-GAK), which couples chemically meaningful residue–residue similarity with sequence alignment while decoupling local matches from gap penalties. MD-GAK is a relatively simple kernel. To further demonstrate the robustness of our framework, we also introduce a variant, PMD-GAK, which incorporates a triangular positional prior. As we will show in the experimental section, PMD-GAK can offer additional advantages over MD-GAK, particularly in reducing calibration errors. Since our focus is on uncertainty estimation, we use Gaussian Processes as the predictive model, as both MD-GAK and PMD-GAK can be directly applied within this framework. We demonstrate the effectiveness of our methods through an extensive set of experiments, comparing our fully reproducible approach against state-of-the-art models, and show that it outperforms them across all metrics. Scientific contribution We introduce monomer-aware decoupled global alignment kernels for Gaussian processes (MD-GAK and position-aware PMD-GAK) that align cyclic peptides at the sequence level using chemically rich monomer fingerprints and explicit positional priors, yielding positive-definite similarity measures tailored to permeability modeling. Compared with order-agnostic fingerprint methods, standard global-alignment kernels and state-of-the-art graph and language-model baselines, our alignment-aware GPs provide improved discrimination, probabilistic calibration and scaffold-level robustness under stringent, leakage-controlled cyclic-peptide permeability benchmarks.