Extended Data Fig. 8 |. Bayesian Prior modeling for balancing information gain and ligand discovery in molecule-selection design and error estimation.

a) Sigmoid functional form for the hit-rate model. b-d) Marginal Bayesian prior (teal) and posterior (red) distributions (n=200,000) for each model parameter b) Top, c) Dock₅₀ and d) Slope. e) Estimated hit-rate based on evaluation by the authors of the docked poses before any molecules were tested (brown: mean (n compound = 200, 220, 230, 230, 285, 235, 210, 230, 200) ± stddev. (n experts = 5,4,4,4,4,4,4,4,4)), the prior mean (green), and samples (n=200) from the prior (blue). f) Candidate (blue) and chosen (orange) experimental designs (Inset Designs 1–6), with expected number of hits and information gain for each. g) Expected number of active scaffolds (orange: mean, gray: posterior draws n=200,000) superimposed on the total number of scaffold cluster heads (black). h-i) Marginal distribution of the number of active compounds (h) and scaffolds (i) over the posterior distributions (n=200,000).