arXiv:2603.12666v2 Announce Type: replace Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (LLMs) have recently shown promising results, most existing methods either generate reactants directly or provide only generic product-level analysis, without explicitly reasoning about bond-disconnection strategies that justify specific reactant choices. This paper proposes RetroReasoner, a retrosynthetic reasoning model that captures chemists' strategic disconnection-based thinking. RetroReasoner is tra
The rapid advancements in large language models enable their application to complex scientific reasoning, moving beyond simple pattern recognition.
This development indicates a significant leap in AI's capacity for strategic scientific discovery, potentially accelerating drug design and materials science.
AI's role in chemistry shifts from a predictive tool to a strategic reasoning partner, explicitly mimicking expert human thought processes in retrosynthesis.
- · Pharmaceutical R&D
- · Chemical engineering
- · AI developers in scientific domains
- · Materials science
- · Traditional manual retrosynthesis workflows
Faster and more efficient discovery of novel chemical synthesis pathways and molecules.
Increased speed and reduced cost in drug development and new material creation, leading to accelerated innovation cycles.
Democratization of complex chemical synthesis design, potentially leading to new biotech and pharmaceutical startups with leaner R&D operations.
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