arXiv:2508.10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely on a static pattern-matching paradigm, which limits their ability to perform effective logical decision-making from chemical data, leading to a black-box process. We propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary strengths of Large Language Models and specialized models via p
The increasing sophistication of Large Language Models and the ongoing drive for interpretable AI in scientific domains are converging to enable new approaches in complex chemical synthesis problems.
This development can significantly accelerate drug discovery and materials science by making chemical synthesis more efficient, predictable, and transparent, reducing experimental costs and timelines.
Retrosynthesis, traditionally a black-box process, becomes more interpretable and collaborative through the integration of LLMs with specialized chemical models, shifting towards more logical decision-making.
- · Pharmaceutical industry
- · Materials science
- · AI-driven drug discovery platforms
- · Chemical research and development
More efficient discovery and development of novel molecules and compounds.
Reduced R&D costs and faster time-to-market for new drugs and advanced materials.
Potential for a new era of 'on-demand' molecular design and automated chemical manufacturing, reshaping industrial supply chains.
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Read at arXiv cs.AI