BayesCurveFit: Enhancing Curve Fitting for Early-Stage Compound Screening Using Bayesian Inference

Abstract Accurate curve fitting is central to quantifying dose–response relationships in drug discovery. However, commonly used regression-based methods are ill-suited for early prescreening assays, where measurements are sparse and experimental noise can dominate. Bayesian approaches have been developed previously, but they are rarely optimized for such data-limited, noise-prone conditions. We present BayesCurveFit, a Bayesian framework specifically designed for robust dose–response inference under data scarcity. The workflow integrates calibrated initialization, stochastic optimization, adaptive posterior sampling, and probabilistic mixture modeling within a unified Bayesian pipeline, enabling reliable parameter estimation and uncertainty quantification from few observations. By modeling residuals with a Gaussian–Laplace hybrid distribution, BayesCurveFit remains robust to outliers where ordinary least squares and conventional regression methods fail. Simulation studies and real screening benchmarks demonstrate that BayesCurveFit outperforms state-of-the-art regression-based methods in recovering true dose–response relationships under limited sampling. In addition, a Bayesian measure of significance – posterior error probability – offers interpretable probabilistic confidence for response classification. Together, these features establish a general and easy-to-use Bayesian framework for analyzing dose–response data in early screening experiments. Competing Interest Statement The authors have declared no competing interest. Footnotes Validation methods for assessing method performance were revised and updated. Figures were accordingly updated.