From Feasible to Practical: Pareto-Optimal Synthesis Planning
For chemists and pharmaceutical industry, this work shifts CASP from finding a single feasible route to providing a Pareto front of trade-offs, aligning with real-world multi-objective decision-making.
MORetro* formulates retrosynthesis as a multi-objective search problem and generates Pareto-optimal routes balancing cost, sustainability, toxicity, and yield, recovering true Pareto fronts with optimality guarantees and outperforming single-objective methods on benchmarks.
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 capture trade-offs among user-defined criteria. MORetro* uses weighted scalarization and BO-informed sampling to efficiently navigate the combinatorial search space and prioritize promising trade-offs. Building on multi-objective A*-search, we provide optimality guarantees showing that, for a fixed single-step model, MORetro* recovers the true Pareto front. Across multiple retrosynthesis benchmarks, MORetro* produces diverse, high-quality Pareto fronts, uncovering solutions overlooked by single-objective approaches and better aligning CASP outputs with industrial decision-making.