Title: RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
Venue: NeurIPS 2025
Accepted: yes
Keywords: retrosynthesis

flow matching

discrete data
Abstract: A fundamental challenge in organic chemistry is identifying and predicting the sequence of reactions that synthesize a desired target molecule. Due to the combinatorial nature of the chemical search space, single-step reactant prediction—i.e., single-step retrosynthesis—remains difficult, even for state-of-the-art template-free generative methods. These models often struggle to produce an accurate yet diverse set of feasible reactions in a chemically rational manner. In this paper, we propose RETRO SYNFLOW (RSF), a discrete flow-matching framework that formulates single-step retrosynthesis as a Markov bridge between a given product molecule and its corresponding reactants. Unlike prior approaches, RSF introduces a reaction center identification step to extract intermediate structures, or synthons, which serve as a more informative and structured source distribution for the discrete flow model. To further improve the diversity and chemical feasibility of generated samples, RSF incorporates Feynman-Kac (FK) steering with Sequential Monte Carlo (SMC) resampling at inference time. This approach leverages a learned forward-synthesis reward oracle to guide the generation process toward more promising reactant candidates. Empirically, RSF substantially outperforms the previous state-of-the-art methods in top-1 accuracy. In addition, FK-steering significantly improves round-trip accuracy, demonstrating stronger chemical validity and synthetic feasibility, all while maintaining competitive top-k performance. These results establish RSF as a new leading approach for single-step retrosynthesis prediction.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents RETRO PRODFLOW (RSF), a template‑free framework for single‑step retrosynthesis that is built on discrete flow matching. Two variants are introduced: 1) RSF learns a Markov bridge that maps product graphs directly to reactant graphs; 2) RSF first decomposes the product at a predicted reaction centre and then transports the resulting synthons to full reactants, treating the synthons as a more informative source distribution. At inference time the authors augment both variants with Feynman–Kac steering implemented through Sequential Monte Carlo; a forward‑synthesis model supplies the reward that preferentially resamples reactant sets likely to satisfy round‑trip consistency. On the standard USPTO‑50k benchmark the synthon‑based model reaches 60 % top‑1 exact‑match accuracy, roughly twenty percentage points above the best prior template‑free method, and improves top‑5 round‑trip accuracy by nineteen points over existing baselines, all while using 50 flow steps per sample.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n+ The paper’s main contribution for casting retrosynthesis as a discrete flow‑matching problem is both conceptually clean and technically sound. By marginalising over conditional Markov paths, the authors obtain closed‑form probability velocities on categorical graph tokens, which allows fully likelihood‑based training instead of the surrogate objectives used in diffusion models. Employing synthons as an intermediate state is well motivated from a chemical standpoint and demonstrably boosts accuracy; the two‑stage design also makes the overall system more interpretable to practising chemists. Feynman–Kac steering is applied with care and yields a convincing diversity–feasibility trade‑off without extra training. \n\n+ The manuscript is clearly written, the algorithmic details (transition kernels, reward definition, SMC schedule) are reproducible, and the ablations isolate the contribution of each component.\n\nWeaknesses\n\n- While the gains in exact‑match accuracy are impressive, the experimental study is limited to USPTO‑50k; it remains unclear how the approach scales to larger, noisier corpora or to multi‑product reactions. \n\n- The work inherits a strong dependency on a separately trained reaction‑centre predictor whose errors are not quantified; the end‑to‑end robustness of the pipeline is therefore difficult to assess. \n\n- No compute‑time comparison with diffusion‑based generators is provided, yet SMC resampling with four particles and fifty steps may be expensive in practice.\n\n- Finally, the claim of being the first “flow‑matching framework for retrosynthesis” would benefit from a clearer differentiation from recent GFlowNet work that also learns stochastic policies over reaction graphs.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces RETRO SYNFLOW (RSF), a template free, graph based framework for single step retrosynthesis built on discrete flow matching. The authors adapt Feynman–Kac (FK) steering with Sequential Monte Carlo resampling, guided by a forward synthesis reward that enforces round trip consistency, while enhancing the diversity and chemical feasibility of the results. Experimentally, RSF achieves a state-of-the-art top-1 accuracy of 60.0% on the USPTO-50k benchmark, outperforming prior template-free methods by 20%. The appendix details sensitivity to synthon allocation, number of SMC particles and sampling steps, and provides runtime statistics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The paper is clear, well written, and presents a novel application of discrete flow matching, exploiting its flexibility with respect to source and target distributions\n* First application of discrete flow matching to retrosynthesis; For template-free methods, the paper demonstrates unequivocally strong results. The evaluations and ablations are straightforward and comprehensive\n* The method will scale very well with more data and compute\n\nWeakness:\n* The methodological contribution is not very novel. Discrete flow matching with FK steering is a well-known method. Previous papers have also used synthons for retrosynthesis. Because the method is less novel, the expectation for state of the art results will be higher\n* The advantage of RETRO SYNFLOW over template-based approaches is questionable. Local Retro is competitive and, in some cases, better. The authors claim that template free approaches are more flexible and generalize better, but it is unclear if better generalization shows up in the evaluation.\n* The round trip metrics seem flawed because the method directly optimizes for them, whereas others do not. We can see this in the Figure 5 ablations, where round trip metrics increase with the number of particles, but accuracy does not. Round trip metrics appear to be proxies for quality, but do not have obvious direct applications.\n* Only USPTO 50k is used; no larger or more diverse sets (e.g., USPTO Full, Pistachio)\n* Molecular Transformer accuracy bounds the reward; its errors may bias results but are not quantified\n\nQUESTIONS:\n* What is the scale of the model (eg number of parameters)? How does inference time scale with the number of particles?\n* Have you tested on USPTO Full, MIT Mix or proprietary sets? A small scale study would strengthen the significance claim and could raise the quality score.\n* To clarify, are synthons just the product graph with deleted edges? If so, why is it such a good inductive bias? How many edges are being deleted on average?\n* Can you report the proportion of FK steered outputs that are actually chemically valid (validated with RDKit) but misscored by the oracle? Sensitivity analysis on oracle noise would clarify reliability\n* Please provide inference wall time versus template based baselines, and discuss GPU memory needs for large batch SMC.\n* RSF assumes the top 2 reaction center predictions; how does accuracy degrade if the correct center is ranked lower, and could joint training of center and flow alleviate this?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe task is retrosynthesis, i.e. identification of a  synthesis route  and the class of architecture is that of flow-matching; The authors have considered a Markov bridge that functions to cover the target product and the reactants. They used intermediate structures of the flow, synthons, i.e. intermediate fragments. There is a reaction center predictor that maps the synthons to reactants.\n\nSTRENGTHS AND WEAKNESSES:\nstrengths:\n    While previously addressed using Markov Bridges [1], the paper models the transformation from product to reactants as a continuous-time Markov chain over the discrete space of atoms and bonds using discrete flow matching, to my knowledge, for the first time\n    The authors address diversity in the context of retrosynthesis prediction and use FK-steering to improve round-trip accuracy at inference time.\nIntro does a good job at setting up an open research question on the retrosynthesis; the math is very clear and together with figures explain well the two steps, i.e. 1) mapping products directly to reactants; 2) leverages synthons to facilitate the generation task. The appendix is very informative.\n\nweaknesses:\n    The empirical results are impressive and push the state of the art, however there are a few limitations that the authors should address:\n\n The authors repeatedly describe their methods, including RSF, as \"template-free\", which is somewhat misleading. RSF employs a two-stage process of reaction center identification followed by synthon completion, and I would encourage the authors to consider repositioning the model as a semi-template method. This would align the work more accurately with the established literature [2] and would more clearly highlight that the core component leading to the state-of-the-art results lies in the DFM-based synthon completion module.\nThe reward oracle for FK-steering is an imperfect forward synthesis model. How might false positives and false negatives from this reward model bias the steered generation process? A discussion of this limitation, and perhaps a small-scale analysis of the forward model's accuracy on a subset of the generated reactions, would add some nuance.\nThe finding in Table 3 that removing the product context improves synthon completion accuracy is counter-intuitive. Can the authors provide a hypothesis or an analysis to explain this? Does this point to a limitation in how the denoiser handles conditional information?\nMinor: While not necessary for this rebuttal, I’d be curious to see how RetroBridge performs with the same number of sampling steps used by SynFlow.\nMinor: Line 151 says “Since some atoms present in the product may not appear in the reactants”, it should be the other way around – atoms in the reactants might not be present in the product.\n\n \n\n[1] RetroBridge: Modeling Retrosynthesis with Markov Bridges, Ilia Igashov and Arne Schneuing and Marwin Segler and Michael Bronstein and Bruno Correia, 2024, arxiv 2308.16212,\n\n[2] Learning Graph Models for Retrosynthesis Prediction, Vignesh Ram Somnath and Charlotte Bunne and Connor W. Coley and Andreas Krause and Regina Barzilay, 2021, arxiv 2006.07038.\n\nQUESTIONS:\nSee detailed comments under Weaknesses. Each point can be considered a prompt/question and I am willing to adjust my review if addressed appropriately.\n\nLIMITATIONS:\nPlease consider discussing limitations or avenues for future research at the end of the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-04"}

comments: SUMMARY:
The authors use discrete flow matching for the problem of retrosynthesis prediction, and use also Feynman Kac steering with a forward reaction prediction model.

This is as far as I know the first use of flow  matching and FK steering for this task. Nice!

The model achieves good performance.


Overall this paper should be accepted to the conference after some comments have been addressed

STRENGTHS AND WEAKNESSES:
Strengths:
Interesting model formulation and results. 
Nice use of forward model via FK steering

Weaknesses:
The FK ablation isn’t clear. Most Other baselines in the main results table don’t use the forward model as additional filtering, thus the comparison isn’t fully fair. I suggest to add the results without FK steering to the main table as well

Missing citations to highly relevant work
https://arxiv.org/abs/2405.17656

Other works that have used forward model steering/filtering for synthesis planning:
- Segler er al Nature 2018 
- Coley et al Science 2019
- Schwaller et al Chemical Science 2020  

The chimera model by Maziarz er al https://arxiv.org/pdf/2412.05269 outperforms retrosynflow for all but top1 accuracy, even without an forward model filters or steering (see Extended Data Table1). This should be added to the main results table.


Comment:
This reviewer will increase score after discussion of above points

QUESTIONS:
1) which Codebase have the authors used to compute the metrics?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-13