arXiv:2605.07521v2 Announce Type: replace Abstract: Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or shortest-path metrics. This view is misaligned with real-world practice, where chemists must balance competing objectives such as cost, sustainability, toxicity, and overall yield. To address this, we formulate synthesis planning as a multi-objective search problem and introduce MORetro*, an algorithm that generates a Pareto front of synthesis routes to explicitly captur
The increasing sophistication of AI and computational chemistry techniques allows for more complex, multi-objective optimization in synthesis planning, moving beyond single-route identification.
This development allows chemists to optimize for multiple critical factors simultaneously, leading to more efficient, sustainable, and cost-effective chemical production processes.
Synthesis planning shifts from identifying merely feasible routes to generating a Pareto front of routes, enabling chemists to make data-driven trade-offs based on cost, sustainability, toxicity, and yield.
- · Pharmaceutical industry
- · Chemical manufacturing
- · AI/ML companies in chemistry
- · Sustainable chemistry initiatives
- · Traditional retrosynthesis software
- · Companies with inefficient synthesis processes
Chemical R&D becomes more efficient and less wasteful, reducing development timelines and costs.
The ability to prioritize sustainability and toxicity from the outset accelerates the transition to greener chemistry and safer products.
Novel materials and therapeutics become economically viable to synthesize due to optimized, multi-objective planning, pushing the boundaries of what is manufacturable.
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Read at arXiv cs.AI