A decoupled alignment kernel for peptide membrane permeability predictions

• URL: http://arxiv.org/abs/2511.21566v1
• Date: Wed, 26 Nov 2025 16:40:41 GMT
• Title: A decoupled alignment kernel for peptide membrane permeability predictions
• Authors: Ali Amirahmadi, Gökçe Geylan, Leonardo De Maria, Farzaneh Etminani, Mattias Ohlsson, Alessandro Tibo,
• Abstract summary: We propose a monomer-aware decoupled global alignment kernel (MD-GAK), which couples chemically meaningful residue-residue similarity with sequence alignment.<n>We also introduce a variant, PMD-GAK, which incorporates a triangular positional prior.<n>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.
• Score: 35.849562641740754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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