Schedule Optimization for Chemical Library Synthesis

Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the scheduling problem for chemical library synthesis, where operations from interdependent synthetic routes are scheduled to minimize the makespan—the total duration of the synthesis campaign. We formalize this problem as a flexible job-shop scheduling problem with chemistry-relevant constraints in the form of a mixed integer linear program (MILP), which we then solve in order to design an optimized schedule. The scheduler's ability to produce valid, optimal schedules is demonstrated by 720 simulated scheduling instances for realistically accessible chemical libraries. Reductions in makespan up to 73%, with an average reduction of 38%, are observed compared to the baseline scheduling approach.