RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
Authors: Mianzhi Liu, Fan Xiao, Zhiliang Yu, Huayang Huang, Yuke Li, Yi Yang, Wenbo Liu, Yu Wu
Organizations: School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China · School of Computer Science, Wuhan University, Wuhan 430072, China · College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China · College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
Retrosynthesis is a cornerstone of drug discovery and organic synthesis. While data-driven deep learning models have shown remarkable progress, they autonomously learn reaction patterns from extensive datasets with limited integration of established chemical knowledge as priors. To address this limitation, we introduce RetroMPA, a molecular property-aware, post-hoc enhancement module that injects chemical knowledge into the retrosynthesis pipeline. Rather than functioning as an independent SMILES sequence generator, RetroMPA is a broadly applicable, model-agnostic chemical filter designed to recalibrate and optimize the predictive pathways of existing algorithms. This plug-and-play framework integrates seamlessly with a range of data-driven retrosynthesis methods, enhancing outputs without modifying model architecture or requiring resource-intensive retraining. By leveraging a property-aware latent embedding space, RetroMPA consistently improves top-1 accuracy across eight representative retrosynthesis models by an average of 5.50% on USPTO-50K. Furthermore, we validate its scalability on the large-scale USPTO-Full dataset, achieving an average improvement of about 2.03% across both template-based and template-free architectures. Wet-lab experiments provide preliminary support for the practical utility of the framework. These syntheses confirmed viable, previously unreported substrate combinations for classic reaction paradigms---specifically, Suzuki-Miyaura coupling, Bucherer reaction, and Friedel-Crafts acylation---suggesting that RetroMPA can operate beyond mere data fitting. The code is open-sourced at https://github.com/MengzhouLu/RetroMPA.
Figures & tables
Figure 1: Standard retrosynthesis autoregressive modeling (Existing) vs our proposed RetroMPA (Ours). (a) Autoregressive applied to SMILES: sequential atom token generation from left to right, atom by atom, with limited explicit integration of chemical knowledge as priors; (b) RetroMPA generates molecular property embeddings guided by chemical knowledge, subsequently outputting complete molecules through inverse projection in chemically constrained spaces.
Figure 2: Overview of the three-stage RetroMPA framework. Stage 1: Mol-Former encodes molecules into molecular property embeddings Qm through multimodal molecule–text pretraining. Stage 2: Given the product embedding Qp and previously available or predicted reactants (r1,r2,…,rk) , the Molecular Decoder predicts the embedding of the next reactant rk+1 . Stage 3: The predicted embedding is mapped to molecular SMILES by Inverse Projection.
Method
Original Acc.
+Ours
Δ
Template-based
LocalRetro 2
52.59
57.76 ± 0.06
5.17 ± 0.06
Semi-template-based
GraphRetro 33
53.72
59.54 ± 0.12
5.82 ± 0.12
Template-free
Transformer 15
42.80
50.99 ± 0.16
8.19 ± 0.16
Table 1: Top-1 accuracy on USPTO-50K dataset. Δ indicates the improvement after using RetroMPA. Results of our method are averaged over 3 random seeds, reported as mean ± standard deviation.
Method
Original Acc.
+Ours
Δ
Template-based
LocalRetro 2
40.12
42.92 ± 0.04
2.80 ± 0.04
Template-free
EditRetro 11
51.10
52.36 ± 0.03
1.26 ± 0.03
Table 2: Top-1 accuracy on USPTO-Full dataset. Δ indicates the improvement after using RetroMPA. Results of our method are averaged over 3 random seeds, reported as mean ± standard deviation.
Figure 3: Performance enhancement achieved by RetroMPA across various base models on USPTO-50K. The gray bars indicate the original top-1 accuracy of the base models (left y -axis), arranged in ascending order of their inherent capabilities. The red line illustrates the absolute accuracy improvement ( Δ ) conferred by RetroMPA (right y -axis).
Figure 4: Correlation analysis between the performance of base models and the magnitude of improvement delivered by RetroMPA on USPTO-50K. The negative Pearson correlation ( r=−0.96 ) shows a strong negative association.
Table 7
Source
EditRetro (base model)
1.0x
2.0x
4.0x
8.0x
PubChem
60.34
64.75
64.49
64.07
63.61
ChEBI-20
60.34
64.75
64.42
63.93
63.41
Table 5: Top-1 accuracy of EditRetro and RetroMPA with different dictionary scale multipliers, where additional molecules are sampled from PubChem (first row) and ChEBI-20 (second row).
Figure 5: Experimentally Validated Previously Unreported Substrate Combinations Predicted by RetroMPA Using Dynamic Molecular Dictionary. RetroMPA, leveraging its dynamic molecular dictionary, predicted three unreported substrate combinations, including Suzuki–Miyaura coupling, Bucherer reaction, and Friedel–Crafts acylation.
Figure 6: Illustrative examples of RetroMPA refinements driven by molecular property awareness.
Figure 7: Case study of retrieved molecules. The columns show the product, the first reactant, the top three nearest-neighbor candidates for the second reactant, and the ground-truth second reactant. Shared substructures are highlighted in blue.
Figure 12
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure S1 : Introduction of datasets.
Figure S2 : ¹H NMR spectra of all chemical products.
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 trained with supervised fine-tuning and reinforcement learning. For supervised fine-tuning, SyntheticRetro generates structured disconnection rationales paired with reactant predictions. For reinforcement learning, a round-trip reward evaluates predicted reactants by passing them through a forward synthesis model and rewarding predictions that reconstruct the original product. RetroReasoner can also be applied to multi-step retrosynthetic planning by incorporating it into a parallelized Monte Carlo tree search framework, reducing search time while increasing the number and diversity of valid synthetic pathways. Experimental results show that RetroReasoner outperforms prior baselines, including not only molecular LLMs but also retrosynthesis-specific expert models, and generates a broader range of feasible reactant proposals, especially for challenging reaction instances. The code is available at https://github.com/KU-AGI/RetroReasoner.
Hanbum Ko, Chanhui Lee, Ye Rin Kim +4
Department of Artificial Intelligence, Korea University · Materials Intelligence Lab, LG AI Research · Department of Statistics, Korea University
Single-step retrosynthesis needs both accurate first-ranked suggestions and candidate lists that are rich enough for downstream selection. We study this as a proposal-selection decomposition. Our system, RETROSPECT, combines a single Transformer proposal model, which we call the ChemAlign Transformer, with a LambdaMART reranker over structural, reaction-template, upstream-score, and optional DFT-derived descriptors. The generator is trained with hybrid root-aligned and random SMILES augmentation, Pre-LayerNorm, tied embeddings, exponential moving average weights, and a differentiable atom-balance auxiliary loss. On the full USPTO-50K test set of 5,007 reactions, the generator reaches 55.00% top-1 and 86.18% top-10 exact-match accuracy with 99.86% top-1 validity. On the merged candidate-pool benchmark used for reranking, which contains 5,007 test products and about 111 candidates per product, a LambdaMART model trained on the structural feature set reaches 59.4% top-1 with 0.7171 mean reciprocal rank. Feature ablations show that upstream proposal score and template-frequency statistics provide most of the reranking signal, while DFT and reaction-center DFT features provide smaller and less consistent gains. These results support a modular view of retrosynthesis: stronger single-model proposal and learned candidate selection are complementary, and the proposal model can serve as a drop-in component for ensemble systems such as RetroChimera (Maziarz et al., 2024)
Raja Sekhar Pappala, Shreyas Vinaya Sathyanarayana, Ronit Kumar Choudhary +2
Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves molecular graph transformations that can change both connectivity and graph size. These transformations may introduce reactant components absent from the target while revising the product-derived structure. To model these transformations, we propose RetroGEF, a flow-based generative model for single-step retrosynthesis. Starting from the target molecule, it constructs possible reactants by adding atoms and changing bonds in the molecular graph. RetroGEF models molecular transformations and changes in graph size within the same generative process, rather than relying on a fixed-size graph canvas. It learns this process directly from product--reactant pairs without requiring a prescribed edit order. Experiments on representative retrosynthesis benchmarks demonstrate that RetroGEF achieves state-of-the-art performance.
Xiaozhuang Song, Xuemin Chen, Xinjian Zhao +2
The Chinese University of Hong Kong, Shenzhen · Shanghai AI Laboratory