pith. sign in

arxiv: 1907.01356 · v2 · pith:SE6CUU4Ynew · submitted 2019-07-02 · ⚛️ physics.chem-ph · cs.CL· cs.LG

Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

Shuangjia Zheng , Jiahua Rao , Zhongyue Zhang , Jun Xu , Yuedong Yang This is my paper

Pith reviewed 2026-05-25 10:32 UTC · model grok-4.3

classification ⚛️ physics.chem-ph cs.CLcs.LG
keywords retrosynthesistransformerneural networksmachine translationself-correctionsynthesis planningdeep learningmolecular notation
0
0 comments X

The pith

Self-corrected Transformer neural networks achieve 59% accuracy in retrosynthesis prediction by treating it as a translation task.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a method called SCROP that uses the Transformer architecture to predict retrosynthetic reactions without relying on templates. It frames the task as translating product molecules into reactant molecules using linear notations. A separate neural network corrects syntax errors in the predictions. This yields 59% accuracy on a standard dataset, surpassing previous deep learning approaches by more than 21% and template-based methods by over 6%. The improvement is even larger, 1.7 times, for molecules not seen during training.

Core claim

The authors claim that converting retrosynthesis to a machine translation problem using Transformers, combined with a neural syntax corrector, produces a template-free predictor that reaches 59.0% accuracy on benchmarks and performs substantially better on novel compounds than existing methods.

What carries the argument

The self-corrected retrosynthesis predictor (SCROP), which applies Transformer-based sequence translation followed by syntax correction.

If this is right

  • Retrosynthesis can be performed without predefined reaction templates.
  • Accuracy gains are particularly pronounced for compounds absent from the training set.
  • The method could reduce the time chemists spend on planning synthetic routes.
  • Machine translation techniques from natural language processing apply directly to molecular sequences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the syntax corrector is the key, similar correction steps could improve other sequence-based chemical predictors.
  • Testing on more recent or larger reaction datasets would reveal if the gains hold beyond the standard benchmark.
  • Combining this predictor with forward synthesis models might enable closed-loop planning systems.

Load-bearing premise

The reported accuracy improvements stem from the self-correction mechanism and the translation framing rather than differences in data processing or evaluation methods.

What would settle it

Running the model without the syntax corrector on the same benchmark and observing whether accuracy falls below 59% or matches other methods would test the contribution of the correction step.

read the original abstract

Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces SCROP, a template-free retrosynthesis model that frames reactant prediction as SMILES translation using a Transformer, augmented by a separate neural syntax corrector. It reports 59.0% top-1 accuracy on a standard benchmark (USPTO-50K), claiming >21% improvement over prior deep-learning methods, >6% over template-based methods, and 1.7× higher accuracy on compounds absent from the training set.

Significance. If the numerical gains can be shown to arise specifically from the self-correction module under identical data splits, preprocessing, and evaluation protocols as the cited baselines, the work would constitute a useful incremental advance in template-free retrosynthesis, especially regarding generalization. The core idea of coupling a seq2seq model with a learned corrector is straightforward and potentially extensible; however, the manuscript supplies none of the controls needed to attribute the reported improvements to that component.

major comments (3)
  1. [Abstract, §3] Abstract and §3 (Experiments): the headline 59.0% top-1 accuracy and the >21% / >6% / 1.7× comparative claims are presented without stating the precise USPTO-50K split, SMILES canonicalization procedure, or beam-search settings used. Because these choices directly affect the numbers reported for both the proposed model and the baselines it cites, the attribution of gains to the self-correction module cannot be verified from the given information.
  2. [§3.2, Table 2] §3.2 and Table 2: no ablation is reported that isolates the syntax-corrector network from the base Transformer. Without this control it is impossible to determine whether the reported accuracy lift is due to the novel component or to differences in training regime, data handling, or hyper-parameters.
  3. [§3.3] §3.3: the phrase “compounds not appearing in the training set” is not defined (product SMILES only, reactant SMILES, or full reaction). This ambiguity renders the 1.7× generalization claim non-reproducible and prevents direct comparison with prior work that uses explicit product-only or reaction-level novelty splits.
minor comments (2)
  1. [§3] The manuscript should include the exact train/validation/test split files or a reference to the canonical Liu et al. (2017) split used by most subsequent retrosynthesis papers.
  2. [Figure 3] Figure 3 and the associated text would benefit from an explicit statement of how invalid SMILES are counted in the accuracy metric (i.e., whether they are treated as failures or post-corrected).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful reading and constructive suggestions. We address each major comment below and will revise the manuscript accordingly to improve reproducibility and clarify the contributions of the self-correction module.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (Experiments): the headline 59.0% top-1 accuracy and the >21% / >6% / 1.7× comparative claims are presented without stating the precise USPTO-50K split, SMILES canonicalization procedure, or beam-search settings used. Because these choices directly affect the numbers reported for both the proposed model and the baselines it cites, the attribution of gains to the self-correction module cannot be verified from the given information.

    Authors: We agree that these details are required for reproducibility and fair comparison. In the revised manuscript we will explicitly document the USPTO-50K split (standard 80/10/10 protocol matching the cited baselines), the SMILES canonicalization procedure (RDKit with default kekulization), and the beam-search settings (beam size 10) used for both SCROP and the re-implemented baselines. This will enable direct verification of the reported numbers under identical protocols. revision: yes

  2. Referee: [§3.2, Table 2] §3.2 and Table 2: no ablation is reported that isolates the syntax-corrector network from the base Transformer. Without this control it is impossible to determine whether the reported accuracy lift is due to the novel component or to differences in training regime, data handling, or hyper-parameters.

    Authors: The referee correctly identifies the absence of this control. We will add an ablation study to the revised §3.2 and Table 2 that reports the top-1 accuracy of the base Transformer (identical architecture, training regime, and data handling) both with and without the syntax-corrector network. This will isolate the contribution of the self-correction module under matched conditions. revision: yes

  3. Referee: [§3.3] §3.3: the phrase “compounds not appearing in the training set” is not defined (product SMILES only, reactant SMILES, or full reaction). This ambiguity renders the 1.7× generalization claim non-reproducible and prevents direct comparison with prior work that uses explicit product-only or reaction-level novelty splits.

    Authors: We will revise §3.3 to define the split unambiguously: a product-level novelty split in which a test product SMILES does not appear in the training set (reactants may or may not be novel). We will also state the exact construction procedure and confirm that the 1.7× figure is computed on this product-only novelty subset, enabling direct comparison with prior product-level generalization results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical accuracy on held-out benchmark is independent of model definition

full rationale

The paper frames retrosynthesis as a sequence-to-sequence translation task solved by a Transformer plus a separate syntax-corrector network. Training occurs on a standard dataset (USPTO-50K) and performance is measured by top-1 accuracy on an explicitly held-out test partition. These accuracy figures are computed quantities external to the architecture; they are not obtained by re-arranging fitted parameters, re-labeling training statistics, or invoking a self-citation that itself assumes the target result. No equation or claim reduces the reported 59 % accuracy or the 1.7× unseen-compound gain to a definitional identity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The claim rests on the domain assumption that linear molecular notations are sufficient for sequence modeling and on standard neural-network training assumptions; no new physical entities or ad-hoc constants are introduced.

free parameters (1)
  • Transformer model weights and hyperparameters
    Learned during training on the reaction dataset; no specific fitted values reported in abstract.
axioms (1)
  • domain assumption Molecular structures can be faithfully represented by linear string notations suitable for sequence-to-sequence learning.
    Invoked when the retrosynthesis task is converted to a machine translation problem.

pith-pipeline@v0.9.0 · 5695 in / 1215 out tokens · 46805 ms · 2026-05-25T10:32:06.103655+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages · 4 internal anchors

  1. [1]

    Journal of the Chemical Society, Transactions 1917, 111, 762-768

    Robinson, R., LXIII.—A synthesis of tropinone. Journal of the Chemical Society, Transactions 1917, 111, 762-768

    work page 1917

  2. [2]

    J.; Wipke, W

    Corey, E. J.; Wipke, W. T., Computer-assisted design of complex organic syntheses. Science 1969, 166, 178-192

    work page 1969

  3. [3]

    J., General methods for the construction of complex molecules

    Corey, E. J., General methods for the construction of complex molecules. Pure and Applied chemistry 1967, 14, 19-38

    work page 1967

  4. [4]

    J.; Long, A

    Corey, E. J.; Long, A. K.; Rubenstein, S. D., Computer-assisted analysis in organic synthesis. Science 1985, 228, 408-418

    work page 1985

  5. [5]

    Segler, M. H. S.; Preuss, M.; Waller, M. P., Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018, 555, 604-610

    work page 2018

  6. [6]

    D.; Glorius, F., A robustness screen for the rapid assessment of chemical reactions

    Collins, K. D.; Glorius, F., A robustness screen for the rapid assessment of chemical reactions. Nature chemistry 2013, 5, 597

    work page 2013

  7. [7]

    D.; Zentgraf, M.; Kriegl, J

    Christ, C. D.; Zentgraf, M.; Kriegl, J. M., Mining electronic laboratory notebooks: analysis, retrosynthesis, and reaction based enumeration. J Chem Inf Model 2012, 52, 1745-1756

    work page 2012

  8. [8]

    P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B

    Szymkuc, S.; Gajewska, E. P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B. A., Computer -Assisted Synthetic Planning: The End of the Beginning. Angew Chem Int Ed Engl 2016, 55, 5904-5937

    work page 2016

  9. [9]

    P.; Law, J.; Mirzazadeh, M.; Ravitz, O.; Simon, A., Computer ‐aided synthesis design: 40 years on

    Cook, A.; Johnson, A. P.; Law, J.; Mirzazadeh, M.; Ravitz, O.; Simon, A., Computer ‐aided synthesis design: 40 years on. Wiley Interdisciplinary Reviews: Computational Molecular Science 2012, 2, 79-107

    work page 2012

  10. [10]

    D.; Gasteiger, J., Computer‐assisted planning of organic syntheses: the second generation of programs

    Ihlenfeldt, W. D.; Gasteiger, J., Computer‐assisted planning of organic syntheses: the second generation of programs. Angew Chem Int Ed Engl 1996, 34, 2613-2633

    work page 1996

  11. [11]

    H., Computer-aided organic synthesis

    Todd, M. H., Computer-aided organic synthesis. Chemical Society Reviews 2005, 34, 247-266

    work page 2005

  12. [12]

    A.; Azencott, C.-A.; Chen, J

    Kayala, M. A.; Azencott, C.-A.; Chen, J. H.; Baldi, P., Learning to predict chemical reactions. J Chem Inf Model 2011, 51, 2209-2222

    work page 2011

  13. [13]

    r.; Huerta, F.; Hutchings, M

    Bøgevig, A.; Federsel, H.-J. r.; Huerta, F.; Hutchings, M. G.; Kraut, H.; Langer, T.; Lö w, P.; Oppawsky, C.; Rein, T.; Saller, H., Route design in the 21st century: The IC SYNTH software tool as an idea generator for synthesis prediction. Organic Process Research & Development 2015, 19, 357-368

    work page 2015

  14. [14]

    W.; Green, W

    Coley, C. W.; Green, W. H.; Jensen, K. F., Machine Learning in Computer-Aided Synthesis Planning. Acc Chem Res 2018, 51, 1281-1289

    work page 2018

  15. [15]

    Segler, M. H. S.; Waller, M. P., Neural -Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. Chemistry 2017, 23, 5966-5971

    work page 2017

  16. [16]

    Segler, M. H. S.; Waller, M. P., Model ling Chemical Reasoning to Predict and Invent Reactions. Chemistry 2017, 23, 6118-6128

    work page 2017

  17. [17]

    W.; Rogers, L.; Green, W

    Coley, C. W.; Rogers, L.; Green, W. H.; Jensen, K. F., Computer -Assisted Retrosynthesis Based on Molecular Similarity. ACS Cent Sci 2017, 3, 1237-1245

    work page 2017

  18. [18]

    A., Automatic mapping of atoms across both simple and complex chemical reactions

    Jaworski, W.; Szymkuc, S.; Mikulak -Klucznik, B.; Piecuch, K.; Klucznik, T.; Kazmierowski, M.; Rydzewski, J.; Gambin, A.; Grzybowski, B. A., Automatic mapping of atoms across both simple and complex chemical reactions. Nat Commun 2019, 10, 1434

    work page 2019

  19. [19]

    L.; Chen, D

    Chen, W. L.; Chen, D. Z.; Taylor, K. T., Automatic reaction mapping and reaction center detection. Wiley Interdisciplinary Reviews: Computational Molecular Science 2013, 3, 560-593

    work page 2013

  20. [20]

    M.; Sayle, R

    Schneider, N.; Lowe, D. M.; Sayle, R. A.; Landrum, G. A., Development of a novel fingerprint for chemical reactions and its application to large -scale reaction classification and similarity. J Chem Inf Model 2015, 55, 39-53

    work page 2015

  21. [21]

    Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions

    Nam, J.; Kim, J. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions. arXiv preprint arXiv:1612.09529 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  22. [22]

    ACS Cent Sci 2017, 3, 1103-1113

    Liu, B.; Ramsundar, B.; Kawthekar, P.; Shi, J.; Gomes, J.; Luu Nguyen, Q.; Ho, S.; Sloane, J.; Wender, P.; Pande, V ., Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models. ACS Cent Sci 2017, 3, 1103-1113

    work page 2017

  23. [23]

    Attention Is All You Need

    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention Is All You Need. arXiv preprint arXiv:1706.03762 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  24. [24]

    A., Molecular transformer for chemical reaction prediction and uncertainty estimation

    Schwaller, P.; Laino, T.; Gaudin, T.; Bolgar, P.; Bekas, C.; Lee, A. A., Molecular transformer for chemical reaction prediction and uncertainty estimation. arXiv preprint arXiv:1811.02633 2018

    work page arXiv 2018

  25. [25]

    M., Extraction of chemical structures and reactions from the literature; University of Cambridge 2012

    Lowe, D. M., Extraction of chemical structures and reactions from the literature; University of Cambridge 2012

    work page 2012

  26. [26]

    A., What's What: The (Nearly) Definitive Guide to Reaction Role Assignment

    Schneider, N.; Stiefl, N.; Landrum, G. A., What's What: The (Nearly) Definitive Guide to Reaction Role Assignment. J Chem Inf Model 2016, 56, 2336-2346

    work page 2016

  27. [27]

    M.; Basak, S

    Hawkins, D. M.; Basak, S. C.; Mills, D., Assessing model fit by cross -validation. Journal of chemical information and computer sciences 2003, 43, 579-586

    work page 2003

  28. [28]

    W.; Murcko, M

    Bemis, G. W.; Murcko, M. A., The properties of known drugs. 1. Molecular frameworks. Journal of medicinal chemistry 1996, 39, 2887-2893

    work page 1996

  29. [29]

    In Neural word embedding as implicit matrix factorization, Advances in neural information processing systems, 2014; pp 2177-2185

    Levy, O.; Goldberg, Y . In Neural word embedding as implicit matrix factorization, Advances in neural information processing systems, 2014; pp 2177-2185

    work page 2014

  30. [30]

    J Chem Inf Model 2019, 59, 914-923

    Zheng, S.; Yan, X.; Yang, Y .; Xu, J., Identifying Structure-Property Relationships through SMILES Syntax Analysis with Self -Attention Mechanism. J Chem Inf Model 2019, 59, 914-923

    work page 2019

  31. [31]

    Deep Residual Learning for Image Recognition

    He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385 2015

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  32. [32]

    S.; Morton, T

    Ow, P. S.; Morton, T. E., Filtered beam search in scheduling. The International Journal Of Production Research 1988, 26, 35-62

    work page 1988

  33. [33]

    Found in Translation

    Schwaller, P.; Gaudin, T.; Lanyi, D.; Bekas, C.; Laino, T., "Found in Translation": predicting outcomes of complex organic chemistry reactions using neural sequence-to- sequence models. Chem Sci 2018, 9, 6091-6098

    work page 2018

  34. [34]

    T.; Wu, S

    Ng, H. T.; Wu, S. M.; Briscoe, T.; Hadiwinoto, C.; Susanto, R. H.; Bryant, C. In The CoNLL -2014 shared task on grammatical error correction , Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, 2014; pp 1-14

    work page 2014

  35. [35]

    http://www.rdkit.org

    RDKit: Open-source cheminformatics, Version: 2018-09-3. http://www.rdkit.org

    work page 2018

  36. [36]

    Klein, G.; Kim, Y .; Deng, Y .; Senellart, J.; Rush, A. M. OpenNMT: Open-Source Toolkit for Neural Machine Translation. arXiv preprint arXiv:1701.02810 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017