Margin-calibrated classifier guidance via Sequence Completion Ranking raises multi-step retrosynthesis solve rates from 16.8% to 95.3% on USPTO-190 and unlocks previously unsolvable targets.
Vaucher, Andrea Byekwaso, Philippe Schwaller, Alessandra Toniato, and Teodoro Laino
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MORetro* uses weighted scalarization and BO-informed sampling on multi-objective A* search to produce Pareto-optimal synthesis routes with optimality guarantees for fixed single-step models.
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Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
Margin-calibrated classifier guidance via Sequence Completion Ranking raises multi-step retrosynthesis solve rates from 16.8% to 95.3% on USPTO-190 and unlocks previously unsolvable targets.
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From Feasible to Practical: Pareto-Optimal Synthesis Planning
MORetro* uses weighted scalarization and BO-informed sampling on multi-objective A* search to produce Pareto-optimal synthesis routes with optimality guarantees for fixed single-step models.