  Small molecules exhibiting desirable property profiles are often discovered
through an iterative process of designing, synthesizing, and testing sets of
molecules. The selection of molecules to synthesize from all possible
candidates is a complex decision-making process that typically relies on expert
chemist intuition. We propose a quantitative decision-making framework,
SPARROW, that prioritizes molecules for evaluation by balancing expected
information gain and synthetic cost. SPARROW integrates molecular design,
property prediction, and retrosynthetic planning to balance the utility of
testing a molecule with the cost of batch synthesis. We demonstrate through
three case studies that the developed algorithm captures the non-additive costs
inherent to batch synthesis, leverages common reaction steps and intermediates,
and scales to hundreds of molecules. SPARROW is open source and can be found at
http://github.com/coleygroup/sparrow.
