Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction

• URL: http://arxiv.org/abs/2505.18890v1
• Date: Sat, 24 May 2025 22:31:49 GMT
• Title: Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction
• Authors: Morteza Rakhshaninejad, Mira Jurgens, Nicolas Dewolf, Willem Waegeman,
• Abstract summary: We analyze three cluster-conditioned marginal conformal prediction methods for drug-target interaction prediction.<n>Group-conditioned CP works well when one entity is familiar, but residual-driven clustering provides robust uncertainty estimates even in sparse or novel scenarios.<n>These results highlight the potential of cluster-based CP for improving DTI prediction under uncertainty.
• Score: 1.474945380093949
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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