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 r

SUBMITTER: Du N 

PROVIDER: S-EPMC12637442 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature