Accurate predictive uncertainty quantification (pUQ) is critical for assessing chemical property predictions. Incorrect quantification could lead to costly synthesis and characterization of drug candidates destined to fail, or result in misguided environmental regulations.

This work integrates Conformal Quantile Regression (CQR) with graph neural networks to achieve accurate molecular property prediction and well-calibrated predictive uncertainty quantification. By combining conformal prediction with quantile regression, CQR provides model-agnostic, distribution-free prediction intervals with valid coverage for finite samples. We benchmark it against established pUQ approaches, including ensembling, Monte Carlo dropout sampling, mean-variance estimation, and evidential regression.

Our experiments on datasets spanning quantum mechanics, physical chemistry, and pharmacokinetics show that CQR generally provides strong calibration while maintaining competitive predictive accuracy and uncertainty-ranking performance. It also effectively captures heteroskedastic noise across the data, which is a key requirement for robust pUQ. Furthermore, we demonstrate its efficacy with an uncertainty-guided retrospective virtual screening experiment for antibiotic discovery, achieving hit rates above 90%.