Confidence is the key: how conformal prediction enhances the generative design of permeable peptides

2026-05-07

AI Summary

This work addresses the unreliability of optimization directions in reinforcement learning–driven cyclic peptide generation, which often arises when permeability predictors are extrapolated beyond their domain of applicability. To mitigate this issue, the study introduces conformal prediction into molecular generative design for the first time, constructing an uncertainty-aware permeability predictor integrated within a reinforcement learning framework such as PepINVENT. By dynamically constraining the generation process to high-confidence chemical regions at user-specified confidence levels, the proposed method substantially reduces futile exploration and enhances both the reliability and efficiency of designing highly permeable cyclic peptides. Furthermore, it provides statistically calibrated uncertainty guarantees for the predicted properties of candidate molecules.

Abstract

Generative models coupled with reinforcement learning (RL), such as REINVENT and PepINVENT, have emerged as a powerful framework for de novo molecular design. During the ideation process these generative frameworks utilize various predictive models as part of the optimization objectives. However, the utility of the predictive models can be limited by their domain of applicability. When RL is used to explore the chemical space with predictive models, it can suggest molecules that lie outside the predictor's domain of applicability. As a result, the predictions may become less reliable, potentially steering designs into high reward but also high uncertainty chemical spaces. This is particularly pronounced for cyclic peptides which show therapeutic promise due to their modifiability and large interaction surfaces but are understudied compared to small molecules. While passive membrane permeation in cyclic peptides has attracted interest, identifying optimal permeable designs remains challenging yet crucial for targeting intracellular sites. We present an RL-guided generative framework that designs permeable cyclic peptides using an uncertainty-aware permeability predictor as the scoring component. To address predictive uncertainty, especially impacted by novel chemistry, we integrate conformal prediction (CP) as our uncertainty quantification method. CP assesses designs based on the calibrated model under a user-defined confidence level. We demonstrate that rewarding generated peptides with CP-informed predictions improves both reliability and efficiency of peptide optimization process. This also discourages exploration outside the predictor's applicability domain. This approach bridges the gap between predictive uncertainty and RL-guided exploration, showing how generative modelling and conformal prediction can be combined for the first time.

Problem

• cyclic peptides
• membrane permeability
• predictive uncertainty
• domain of applicability
• generative design

Innovation

• conformal prediction
• reinforcement learning
• generative design
• cyclic peptides
• predictive uncertainty