Schedule optimization for chemical library synthesis.

Area of Science:

• Organic Chemistry
• Chemical Engineering
• Operations Research

Background:

• Automated chemistry platforms are crucial for large-scale organic synthesis and compound library generation.
• Efficient execution of synthesis operations is key to platform productivity.
• Scheduling synthesis operations for interdependent routes presents a significant challenge.

Purpose of the Study:

• To address the scheduling problem for chemical library synthesis.
• To minimize the makespan (total duration) of organic synthesis campaigns.
• To develop an optimized scheduling strategy for automated chemistry platforms.

Main Methods:

• Formalizing the chemical library synthesis scheduling as a flexible job-shop scheduling problem.
• Incorporating chemistry-relevant constraints into a mixed integer linear program (MILP).
• Solving the MILP to generate optimized synthesis schedules.

Main Results:

• Demonstrated the scheduler's ability to produce valid and optimal schedules across 720 simulated instances.
• Achieved makespan reductions of up to 58% compared to baseline approaches.
• Observed an average makespan reduction of 20% for realistically accessible chemical libraries.

Conclusions:

• The developed MILP-based scheduling approach effectively optimizes chemical library synthesis.
• Significant reductions in synthesis campaign duration are achievable through optimized scheduling.
• This work provides a robust method for enhancing the efficiency of automated organic synthesis.