BayesCurveFit: Enhancing Curve Fitting in Drug Discovery with 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.