Which approach delivers the best accuracy?
Chemistry-aware language models that are fine-tuned on reaction data and trained with chemical reasoning outperform both retrieval-augmented and standard fine-tuned models. On the USPTO-50K benchmark, a reasoning-driven LLM (RetroDFM-R) achieved 65.0% top-1 accuracy [1], and a chemistry-specialized LLM (ECNU-ChemGPT) reached 68.3% [4]. Top-1 accuracy means the model's single best guess matches the known correct precursor—so in about two-thirds of test cases, the model's first suggestion is right.
Retrieval-based methods, which pull relevant text from the literature to augment predictions, also improve over models trained only on molecular data, but they don't reach the same accuracy. TextReact, a retrieval-augmented approach, significantly outperformed state-of-the-art chemoinformatics models on one-step retrosynthesis [6], but the gains are more modest than the fine-tuned LLMs' top-1 scores. The takeaway: if your priority is raw predictive accuracy, a chemistry-aware LLM is the way to go.
What role does human review play?
Even the best models need human oversight because they can produce chemically implausible or unintuitive suggestions. Double-blind human assessments of RetroDFM-R's predictions confirmed that they are chemically plausible and practically useful [1], but that doesn't mean they're always correct. Another study found that fine-tuned models occasionally deviate from expected reaction pathways, showing a 'creative' but sometimes wrong approach [5].
Human review is especially important when the model's suggestions are used in real synthesis. The disconnection-prompt method [3] was designed to give chemists more control over the model's suggestions, reducing bias and increasing diversity—but it still relies on a chemist to choose the right disconnection site. In practice, experts should treat model outputs as strong starting points, not final answers, and verify them against their own knowledge and lab conditions.
How do these methods compare in practice?
Fine-tuning a general-purpose LLM on reaction data is a solid baseline: one study fine-tuned a model on USPTO-50K and successfully proposed retrosynthetic pathways for about 92% of test cases [5]. That sounds high, but it includes cases where the model identified the right reaction type and center, not necessarily the exact precursor—so it's a broader 'plausible pathway' metric, not top-1 accuracy.
Retrieval augmentation is useful when you have access to a large corpus of chemical literature and want to incorporate external knowledge without retraining. TextReact showed that aligning retrieved text with molecular representations improves performance over models trained only on molecular data [6]. However, it doesn't match the accuracy of dedicated chemistry LLMs, which are trained on massive reaction datasets and can reason about chemical rules [1][4].
The strongest results come from combining fine-tuning with chemical reasoning and tool use. ChemCrow, an LLM agent with access to 18 chemistry tools, autonomously planned and executed real syntheses [2], showing that integrating external tools can push LLMs beyond pure prediction. ECNU-ChemGPT also uses a multi-model scheduling framework to invoke specialized models for different tasks, achieving top performance [4]. So the best practical setup is a chemistry-aware LLM that can call on external tools and databases, with a human chemist in the loop for final validation.
About These Sources
This answer is built on 6 studies (4 peer-reviewed, 2 preprints) — published from 2023 to 2025, 4 from 2024 or later, 2 in Q1 journals, collectively cited 495 times — selected as the most relevant from 6 studies that passed quality screening, drawn from 33 papers retrieved from a database of over 500 million.
Sources used in this answer
Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
RetroDFM-R, a reasoning-driven LLM trained with reinforcement learning and chemically verifiable rewards, achieved 65.0% top-1 accuracy on USPTO-50K and passed double-blind human assessments for chemical plausibility.
Augmenting large language models with chemistry tools
ChemCrow, an LLM agent with 18 chemistry tools, autonomously planned and executed syntheses of an insect repellent and three organocatalysts, demonstrating that tool integration expands LLM capabilities in chemistry.
Unbiasing Retrosynthesis Language Models with Disconnection Prompts
Using disconnection prompts to steer a retrosynthesis language model improved precursor diversity and achieved a 39% performance improvement over the baseline, giving chemists more control over predictions.
A Large Language Model for Chemistry and Retrosynthesis Predictions
ECNU-ChemGPT, a chemistry-specialized LLM with domain-specific fine-tuning and multi-model scheduling, achieved 68.3% top-1 accuracy on USPTO_50K and reconstructed 13 complete experimental pathways for real drug molecules.
Adapting Language Models for Retrosynthesis Prediction
A fine-tuned LLM on USPTO-50K proposed retrosynthetic pathways for approximately 92% of test cases, though case studies showed occasional deviations from expected reaction pathways.
Predictive Chemistry Augmented with Text Retrieval
TextReact, which retrieves and aligns text descriptions with molecular representations, significantly outperformed state-of-the-art chemoinformatics models on reaction condition recommendation and one-step retrosynthesis.