An algorithmic framework for synthetic cost-aware decision making in molecular design.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Molecule discovery relies on iterative design, synthesis, and testing.
• Selecting molecules for synthesis is complex and often relies on expert intuition.

Purpose of the Study:

• To propose SPARROW, a quantitative decision-making framework for prioritizing molecules.
• To balance expected information gain with synthetic cost in molecule evaluation.

Main Methods:

• SPARROW integrates molecular design, property prediction, and retrosynthetic planning.
• It quantifies the utility of testing a molecule against batch synthesis costs.

Main Results:

• The algorithm captures non-additive costs associated with batch synthesis.
• It effectively leverages common reaction steps and intermediates.
• SPARROW demonstrates scalability to hundreds of molecules.

Conclusions:

• SPARROW provides a data-driven approach to optimize molecule selection in drug discovery.
• This framework enhances the efficiency of identifying molecules with desirable properties.