Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction

TL;DR

This paper evaluates cluster-conditioned conformal prediction methods to improve uncertainty estimation in drug-target interaction models, especially for unseen drug-protein pairs, enhancing reliability in drug discovery applications.

Contribution

It introduces and compares three cluster-conditioned conformal prediction methods, demonstrating their effectiveness over traditional approaches in DTI uncertainty estimation.

Findings

Nonconformity-based clustering yields the tightest confidence intervals.

Cluster-conditioned CP provides robust uncertainty estimates in unseen scenarios.

Group-conditioned CP works well with familiar entities.

Abstract

Accurate drug-target interaction (DTI) prediction with machine learning models is essential for drug discovery. Such models should also provide a credible representation of their uncertainty, but applying classical marginal conformal prediction (CP) in DTI prediction often overlooks variability across drug and protein subgroups. In this work, we analyze three cluster-conditioned CP methods for DTI prediction, and compare them with marginal and group-conditioned CP. Clusterings are obtained via nonconformity scores, feature similarity, and nearest neighbors, respectively. Experiments on the KIBA dataset using four data-splitting strategies show that nonconformity-based clustering yields the tightest intervals and most reliable subgroup coverage, especially in random and fully unseen drug-protein splits. Group-conditioned CP works well when one entity is familiar, but residual-driven clustering provides robust uncertainty estimates even in sparse or novel scenarios. These results highlight the potential of cluster-based CP for improving DTI prediction under uncertainty.