Bayesian Optimization Driven Cancer Drug Dose-Response Curve Discovery

Screening of cancer drugs in personalized medicine and cancer treatment research is expensive. Large libraries of compounds must be evaluated, and multiple doses for each compound need to be tested to assess their viability. We introduce a method exploiting cost-aware Gaussian process Bayesian optimization to reduce the number of experiments and amount of compound spent in screening. The method is utilized to iteratively find suitable medication doses, while staying within a predefined budget. Our approach is validated using synthetic data, and subsequently tested using experimental data.