From Feasible to Practical: Pareto-Optimal Synthesis Planning

TL;DR

This paper introduces MORetro*, a multi-objective synthesis planning algorithm that generates Pareto fronts to balance trade-offs like cost and sustainability, aligning CASP with real-world needs.

Contribution

It formulates synthesis planning as a multi-objective search and develops MORetro*, which efficiently finds Pareto-optimal routes with optimality guarantees.

Findings

MORetro* produces diverse Pareto fronts across benchmarks.

It uncovers solutions overlooked by single-objective methods.

The algorithm aligns CASP outputs with industrial decision-making.

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 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.