BiDRA: Bayesian inference as a robust alternative to non-linear regression for dose-response efficiency metrics assessment.

Motivation: Dose-response metrics such as the half-maximal inhibitory concentration, high-dose-response, and slope are central to drug discovery, yet standard Levenberg-Marquardt fits often produce biased or unsupported values for incomplete curves and lack uncertainty measures. In practice, this forces experimenters to visually inspect each curve fit to judge its reliability, a tedious, subjective, and non-scalable process. Results: Across 421 405 public dose-response experiments from three large pharmacogenomic datasets, shared-concentration viability responses were highly consistent across biological replicates, making it reasonable to expect derived efficiency metrics to show comparable replicate behavior when supported by the data. However, Levenberg-Marquardt fits to incomplete curves often produced unsupported metric values, including inflated rates of observable potency estimates (>70% versus ∼39% complete curves), which could falsely suggest metric-level disagreement between similar replicate responses. BiDRA represents efficacy, potency, and slope as posterior distributions, allowing uncertainty to remain large when the data do not support precise metric inference. This avoids treating uncertainty-dominated responses as conflicting point estimates, exposes unsupported Levenberg-Marquardt estimates, and supports uncertainty-aware compound ranking. We illustrate its utility in a structure-activity relationship screen, demonstrating how posteriors enable robust, criteria-based selection in drug discovery settings. Availability and implementation: BiDRA is implemented in Julia and available at https://github.com/lemieux-lab/bidra_robustness.