RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
Authors: Robin Yadav
Qi Yan
Guy Wolf
Joey Bose
Renjie Liao
Venue: NeurIPS 2025 poster ()
Keywords: retrosynthesis
flow matching
discrete data
Primary area: deep_learning

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.

RETRO SYNFLOW: Discrete Flow Matching for
Accurate and Diverse Single-Step Retrosynthesis
Robin Yadav! robiny128student. ubc. ca
Qi Yan qi. yan ubc. ca
Guy Wolf wolfguy@mila quebec
Avishek Joey B,
Renjie Liao
j joey mess@maal..mcgill... ca
rjliaoßece. ubc. ca
UBC;
Vector Institute; 3 'Mila; *Université de Montréal; "Canada CIFAR AI Chair; University of Oxford
Abstract
A fundamental problem in organic chemistry is identifying and predicting the series of reactions that synthesize a desired target product molecule. Due to the combi- natorial nature of the chemical search space, single-step reactant prediction-ia. remains challenging even for existing state-of-the-art template-free generative approaches to produce accurate yet diverse set of fea- sible reactions. this paper, we model single-step retrosynthesis planning and introduce RETRO SYNFLOW (RSF) discrete flow-matching framework that builds a Markov bridge between the prescribed target product molecule and the reactant molecule. In contrast to past approaches, RSF employs reaction center identi- fication step to produce intermediate structures known as synthons as more in- formative source distribution for the discrete flow. To further enhance diversity and feasibility of generated samples, we employ Feynman- Kac steering with Sequential Monte Carlo based resampling steer promising generations at inference using a new reward oracle that relies on a forward-synthesis model. Empirically, we demon- strate RSF achieves 60.0% top-1 accuracy, which outperforms the previous SOTA by 20%. We also substantiate the benefits of steering at inference and demonstrate that FK-steering improves top-5 round-trip accuracy by 19% over prior template- free SOTA methods, all while preserving competitive top-k accuracy results.
single-step
Introduction
Retrosynthesis planning is fundamental problem in chemistry that involves decomposing complex target molecule (the product) into simpler, commercially available structures (the reactants) to establish synthesis routes [8, 45). This process crucial for verifying the synthesizability of proposed molecules with desirable properties, particularly in drug discovery [43]. For instance, retrosynthesis is critical for lead optimization medicinal chemistry, which requires designing efficient synthetic routes to modify chemical structures to enhance a compound's potency, selectivity, and pharmacokinetic properties [25]. Traditionally, chemists manually identify and validate reactants and pathways, a labor-intensive process exacerbated by the vast search space of transformations from reactant to product molecules. This enduring challenge has driven decades of research in aasputer-assiste retrosynthesis [8], with recent advances in machine learning (ML) enabling more effective exploration the combinatorial reactant space [3, 6, 48, 9]. Such methods are promising in significantly accelerate the drug discovery pipeline.
The current dominant paradigm for ML-based retrosynthesis planning consists of main com- ponents: a single-step retrosynthesis model and a multi-step planning algorithm. These ML-based approaches can be broadly categorized as lemplate-base based and template-free methods. Template-based
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(eactant(s)
Break to Synthon(s)
Reactant(s)
Product
Product
Discrete
Discrete Flow
Flow
Reaction Center
Matching
Predictions
Matching
RPF
RSF
Figure I: An overview of our RETRO PRODFLOW (RPF) and RETRO SYNFLOW (RSF) framework. RPF directly maps product molecule to reactants via discrete flow. RSF first predicts synthons from the product using a reaction center predictor, then maps these synthons to reactants via discrete flow.
methods rely predefined database of reaction templates with hand-crafted specificity [36], which ensures syntactic and chemical validity but often limits diversity and generalization to novel reaction types. In contrast, template-free methods and semi-template methods more flexible and capable of predicting reactions not seen in existing databases [50], offering improved generalization. More recently, template-free and remi-template approaches have emerged as a natural fit for generative modeling techniques, enabling retrosynthesis prediction to be formulated as a conditional generation problem: generating reactants conditioned on given product molecule. Semi-template methods in- crease interpretability by breaking the generation process into two steps by first identifying intermedi- ate molecular structures called synthons and completing synthons to form reactants. While promising, many existing generative methods rely on sequential molecular representations like SMILES [39], and as a result, fail to capture the rich chemical contexts encoded in molecular attributed graphs.
Current work. In this paper, we frame single-step retrosynthesis as learning a transport map from source distribution an intractable target data distribution using finite paired samples. We represent molecules as attributed graphs and propose two template-free/semi www.fffeeeeeeeemmeeeeer -ttmplppet generative models- RETRO PRODFLOW (RPF) and RETRO SYNFLOW (RSF)-based on recent advances in discrete flow matching [14, 21. As discrete flows are flexible in choosing the source distribution, we explore two options as shown in Figure First, in RPF, we use the product distribution di- rectly as the source and learn flow that transforms products to reactants. Second, we leverage pretrained reaction center prediction models to construct an informative source distribution over syn- moons-intermmdiatt molecular fragments obtained by decomposing the product at its reaction center. This reduces the original problem to a simpler condi- tional generation task, i.e., mapping from synthons to reactants, and improves performance. Both models learn continuous-ting Markov chains to stochastically transport product molecules to reactants, benefiting from fast inference and high sample quality. Furhter-
Product
Forward Synthesis Model
Reactant(s)
more, they naturally support scoring and ranking of generated reactants via its stochastic formulation.
Round-Trip Reward
As the space of possible reactants that synthesize into valid products is combinatorially large, fundamen- tal design goal in retrosynthesis is to generate diverse candidate reactants that simultaneously achieve high accuracy. To address this, we leverage Feynman Kac (FK) steering [40], inference-time steering method that guides sampling of flow models towards more feasible and diverse outputs. FK steering employs quential Monte-Carlo (SMC). particle-bant based method that resamples promising intermediate candidates throughout the generation process based reward model to enforce round-trip consistency a
t-0
Inference time
t= 1
Figure 2: Inference time steering with forward-synthesis reward model.
function. We define this reward using a forward-synther standard measure of diversity and feasibility. We highlight the benefits of reward- based inference time steering, demonstrating a 13% improvement in top-5 round-trip accuracy over prior SOTA methods.
In summary, main contributions are listed below.

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1. We propose the first flow matching framework for retrosynthesis, introducing two variants: RETRO PRODFLOW (RPF), which maps products directly reactants, and RETRO SYNFLOOW (RSF), which leverages synthons (intermediate molecular fragments) to simplify the generation task.
2. We improve the diversity and feasibility of generated reactants using FK-steering, an inference-time technique guided by forward-synthesis reward. This yields a 19% gain in top-5 round-trip accuracy over prior template-free methods.
3. We show that using synthons as an inductive prior significantly enhances performance. RSF achieves 60% top-1 accuracy on the USPTO-50k benchmark, outperforming state-of-the-art femplate-free and semi-template methods.
2 Background and preliminaries
Notations. Given vocabulary set X' with d elements, we establish bijection between X' and the index set [d] {1.... d). Accordingly, any discrete data æ drawn from X' can be represented as an integer index in [d]. A categorical distribution over X. denoted Cat(a;; p). is given by p(a i) = p'. where Σi= p² and p¹ 2 0, Vi. A sequence x = (x' ,x'',".,"""" ..).. of n tokens is defined over the product space x We assume dataset of such sequences is sampled from a target data distribution Pdata- Discrete flow matching models, like their continuous counterparts, aim to transport source distribution Psource
Po defined time = 0 to the data distribution Pdata = P1 time
2.1 Discrete Flow Matching
Discrete flow matching operates directly on discrete data, mirroring the construction of flow matching models over continuous spaces. Analogously, our goal is to construct generative probability path, Pt. that interpolates between the source and target distributions. The key insight of flow matching is to construct pt by marginalizing simpler probability paths conditioned on samples from the source and data distributions. A conditional probability path, pc(-[xo, x1) is a time-evolving distribution satisfying Px | x1) 5(x)) and pi (x' [xo
d(x)) ) where xo Po and X1 ~ P1- Conditional probability paths are independent across each dimension of the sequence, with the simplest choice being a convex combination of po (x' Xx. x1) and p1 p1(x' X1).
Pr (xx 1x. x1) Cat(x/: -1-
(1-t)0(x)) t))(2() to(x))), where P. Px x1)
II (x x1). (I)
Similar to the continuous setting, discrete flow matching constructs generating probability velocity ue(:xx,) E RR", which models the rate of probability mass change of the sample x, in each of its n positions. Specifically, we view (xe)osısi as a collection of random variables that form continuous- time Markov Chain (CTMC), jumping between states in X" Each position of x, can be simulated by the following probability transition kernel Pt+Aje (x/ (x,) Thus, sampling from the CTMC and simulating a trajectory from Pt given its velocity u, is straightforward. We start from sample Xu ~ Po from the source distribution and update each dimension with the transition kernel x ~ Pt+ajee (x,) This results in samples from the desired data distribution P1- Analogous to continuous flow matching, ug is constructed by marginalizing the conditional probability velocities that generate the conditional probability paths. For the simple conditional probability paths given by Eq. 1, the marginal probability velocity is (x(',, xx) = (x' - 5(x;)) /(1 t). The intractable posterior distribution, P1 e: known as the probability denoiser, predicts clean sample from intermediate noisy sample x,. We can approximate the denoiser with a neural network pa [x] (x.) and train it by minimizing a cross-entropy loss that forms a weighted evidence lower bound (ELBO) on log P1,0 (x1) [12].
Cat(x) 8(x:) (xf
2.2 Feynman-Kee Steering
Given a trained flow model whose marginal distribution at time is denoted by (x1).. We are interested in sampling from a target distribution that tilts po(xi) using terminal (parametrized) reward function r X [0, 1]. that consumes fully denoised sequences X1.
Penrger (x1) z Po (xx) exp(Ar(xi))
(2)
3

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where À controls the steering intensity, and 2 is a normalization constant. Sampling trajectories in flow models involves discretizing the interval [0, 1] into a grid of timesteps {0, h...., 1 h, } and sampling from the learned transition kernel Pe+hit- To steer the sampling process toward high-reward outcomes, we employ F Kac (FK) steering [40], which modifies the transition kernels using potential functions that favor trajectories 7 T(x:1)) ending with high-reward X1 samples. The FK process begins from a reweighted initial distribution PFK,o (x (xo) X Psource (xo))o(Xo)) and iteratively build tFK, by tilting the transition kernel with potential Ug Ux(x0,- , Xr):
( Z Pe (x,)) po (xox))
II
Ug (x(:s)))
BE (U, a h.)
Since direct sampling from PFK.1 is intractible, we employ Sequential Monte Carlo (SMC) methods. SMC begins with K particles (xx) ) mm= sampled from the source distribution. At each transition step, it updates their importance weights and resamples the particles accordingly. The importance weight for particle m time & h is given by with Pe+aje (x
( x,)U (x.:).
3 Discrete Flow Matching for Retrosynthesis
We model single-step retrosynthesis using discrete flow matching by representing product and reactant molecules as a pair of molecular graphs (G",G") Our focus on the single product setting, i.e., a single product molecule corresponds to a set of reactant molecules. The reactants are represented as a single disconnected graph account for multiple molecules. A molecular graph G = (v, EE) with N atoms consists of: node feature vector v E [K,]", where v' encodes the atom type of atom (out of K,, possible types), and an edge feature matrix E E [K.]Wx,, where E indicates the bond type (among Ke possible types) between atoms i and j.
In this formulation, both and E can be viewed collections of discrete random variables. We aim to learn generative probability path that interpolates between source and the data distributions over graphs of product and reactant pairs. Correspondingly, the design of the conditional probability path of a graph, pe (Gr G1) factorizes over the nodes and edges as follows:
(v Vo. v1) = (1 - ())(v)) + to(1)), Pr (E Eo, E (1 t))(E)))) 15 ()
To complete the specification of discrete flow matching model, we must also define both the source and data distributions. For retrosynthesis, the data distribution, Pdata is simply the distribution of reactant molecules. We explore two choices for the source distribution. The most natural option is to set the source distribution, Psnurce to the empirical distribution of product molecules. This leads to our first model, RETRO PRODFLOOW (RPF), which directly transports product molecules their corresponding reactants. The second option, which we discuss in detail in the following section, sets the source distribution the space of intermediate synthons which acts more informative structural prior. Since some atoms present in the product may not appear in the reactants, we follow RetroBridge [18] and append dummy atoms to the product to ensure alignment of node dimensions. More details are discussed in Appendix A.
The probabilistic formulation of flow matching provides natural way select the most likely reactants for a product molecule out of all possible generations. For a set of N samples, {xi [ generated by the flow matching model for xo Psource. the score of sample X2 is the empirical frequency. This score is an estimation of the probability of x; given Xg, i.e.,
| (x)) E. (- 1|xa) x1
Σ 1=1
- = x1)
3.1
RETRO SYNFLOW
In our approach, we inject more valuable structural information into the flow matching process by setting the distribution of synthons as a more informative source. This is motivated by the two-stage formulation of single-step retrosynthesis: reaction center identification and synthon completion. The stagstage approach improves interpretabilityy by closely mirroring the way expert chemists reason about retrosynthesis. Synthons are hypothetical intermediate molecules representing potential

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reactants [39]. Although a synthon may not correspond to chemically valid molecule, it can be transformed into one by adding suitable leaving groups that account for reactivity.
Synthon generation begins by identifying a reaction center in the product molecule. A reaction center defined pair of atoms ji, in the product satisfying by a bond in the product molecule, and 2) there is no bond between atoms i and j in the reactant(s). We can derive the synthon molecule(s) by deleting the bond that connects the atoms in the reaction center.
criteria: 1) atoms and are connected
Our primary focus lies in the second step of retrosynthesis: applying flow matching for synthon completion. In this case, the source distribution Psource is the distribution of synthons. Given product molecule G", we use a reaction center prediction model to output M potential reaction- candidates. This results in M predictions for the set of synthon(s). Each set of synthon(s) is treated as a single disconnected graph, G To train the discrete flow model, we once again decompose the conditional probability path pa (G [Go G1) over the nodes and edges where Go G and G == G". Therefore, we can model the generating probability velocity and update the tokens of the nodes and edges separately according their respective transition kernels:
modes, (v', vi) 1-t [po(v') ve,
Ve. E.) 8(vi)]
Eij, vv) = 1 (Po(E Ive. E.) 5 5(E
edges,
The denoiser model pa (GI outputs probabilities edges conditioned on noisy state graph G2, an interpolation between synthons and reactant graphs. The stochastic processes over nodes and edges are coupled due to the denoiser model taking the noisy graph input. We can express the outputs of the denoiser model separately edges Po (v v., E,) and po (oEEv, v E.). This leads to a natural objective for discrete flow matching where we minimize the weighted combination of the cross-entropy loss over nodes and edges:
"clean" (reactant) graph over nodes and
the nodes and
(()) = - Pe00opoecGoppplihhacc/.....
Σ log Po (v'l E.) + Po E (E
[v, E.)
where the distribution over time p(t) is sampled uniformly, i.e. 1(0
As in the previous case, we append "dummy" nodes (atoms) to the synthon molecule graph since the reactant molecule graph can have atoms that are not present in the synthon graph. For every synthon graph GÝ, we generate N; sets of reactants. The Ny's and M are hyperparameters. Given a budget of N set of reactants per product, we constrain ΣM N; N. We demon- strate our experiment section that these hyperparameters do not require much tuning, and M = 2 provides SOTA perfor- mance.
Naisy sython
Reactant(s) Prediction
Denoiser
Synthon(s)
Product context
context
Figure 3: Overview of flow match- ing denoiser pa-
We adopt the reaction center prediction model from Shi et al. [39] to identify synthons. We do note however, that any model that outputs synthons could be equivalently used. Prior works such [54, 39, 41] typically treat reaction center identification They use graph neural networks to classify each bond in the product as reactive or not by predicting bond-level reactivity scores or edit scores. During inference, we select the top-M bonds above certain score threshold as candidate reaction centers, yielding M synthon predictions.
bond-level classification task.
3.2 Reward-basee steering
Given the discrete flow matching framework for retrosynthesis above, specify a potential func- tion U2 and reward r(xx) to perform inference time steering. For intermediate steps t < 1, we define:
U, (x(:)) exp (Eimra(x.)) and
ro((x))
U, = exp(Xra(xi))
(I.stto...11.)) U²
This design ensures that РvK,1 X Ptarget while steering intermediate particles toward high-reward trajectories. Furthermore, it selects particles that have the highest accumulated reward.
5

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To perform SMC resampling, we need access to reward oracle, Ts that models the distribution of rewards (x(r(x)) )xx)) generated from the intermediate state x, Fortunately, we can still obtain high-quality estimates from this reward distribution without training separate reward model by querying the flow matching denoiser model (x (xx) instead. Specifically, the intermediate reward is defined as ra (x,) == ( where X1 Eps (x, [x,) [x xx] is the expected x given X,. This choice of intermediate reward can be evaluated efficiently without significant additional computational cost.
Our reward function (Gi) is inspired by round-trip accuracy, which measures the ability of retrosynthesis model to recover diverse and feasible reactants. There may be many different sets of reactants that can synthesize the same product. Top-k round-trip accuracy aims to capture this characteristic of retrosynthesis by quantifying the proportion of feasible reactants(s) among the top-k predictions from the retrosynthesis model. We can assess whether a reaction is feasible by using a forward-synthesis model, which predicts the product molecule produced by a set of reactants. A reaction is deemed feasible the forward model satisfies F(G") = GP, where G" is the predicted re- actants. The reward function serves proxy for round-trip accuracy, encouraging the generation of chemically valid and synthetically feasible reactants. Suppose (GP, G*) is a pair of product and reac- tant molecule graphs. The reward for the intermediate state G. for the flow matching process on graphs is defined r& (G+) I[F(C,) GP) where G1 is the expected G1 (reactants) given GG. With this formulation of the reward, we introduce RETRO PRODFLOW RS, a seward-steered version for RETRO PRODFLOW. Finally, we use Molecular Transformer [35] for the forward-synthesis model.
4 Experiments and Results
We evaluate our proposed methods against state-of-the-ard cemplate-free and template- baase models on standard single-step retrosynthesis benchmarks. Through RETRO SYNFLOW, we aim to highlight the effectiveness of synthons as an inductive prior for generating reactants using flow matching. Additionally, we evaluate RETRO PRODFLOW-RP RS show that inference-time reward-basse steering enhances the diversity and feasibility of predicted reactants. We conduct various ablation studies assessing the performance of Ftesteeni and SMC-based resampling on standard metrics. Unless otherwise stated, we generate N 100 sets of reactants for each input product. Our methods discretize the time interval [0, 1] into T = 50 steps. For SMC resampling, RPF-RS uses particles. For RSF, we use M 2 synthon predictions with N1 = 70 and N2 = 30 for the top-2 synthon predictions respectively. Code is available at: ht https: //gt:///tttbbb com/DSL-Lab/ RetroSynFlow.
4.1 Experimental Setup
Dataset. We trained and evaluated our methods on the USPTO-50K dataset [34], a standard
benchmark for retrosynthesis modelling containing 50k atom-mapped reactions extracted from US patents. We follow the same train/evaluationntest split used by RetroBridge [18] and GLN [9]. As done in Retrobridge, we randomly permute the graph nodes as pre- pre-processing step before input to the flow matching model.
Baselines. We evaluate our methods against both template and template-free baselines. On the template-free side, we compare against graph-based approaches such RetroBridge [18] and G2G [39]. We compare against SMILE string translation approaches such as Tied-Transforma [20], Augmented- hugmented-Transffrnee [48], and SCROP [57]. Our baselines also include methods that combine graph and SMILE representations: GTA [38], MEGAN [33], Dual-TF [46], and Graph2SMILES
[49]. On the template-based side, we compare against state-of-the-arr approaches such GLN [9],
GraphRetro [41]. LocalRetro [4], and RetroGFN [13]. We also compare against Chimera [27],
framework that ensembles multiple different SMILE and graph based models across template and template-free approaches.
Evaluation. We report top-k exact match accuracy, which measures the proportion of reactions where the ground-truth set of reactants is in the top & set of reactants predicted by the model. Following standard practices in prior works, we report A 1,3, 5, 10. Additionally, top-k round-trip accuracy and round-trip coverage to measure reaction feasibility. Given product and reactant molecular graphs (GP, GT), let R = (Gr be the top-k predicted sets of reactants for G", Let P {F(GT))& be the predicted products from the forward model. Then
report

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top-k exact-match accuracy, round-trip accuracy and coverage for the product x are computed as Io E Rl. E L[G F(G and 1 E Pl.
4.2
Main Results
Table 1: Top-k accuracy (exact match) on the USPTO-50K test dataset.
Top-k Accuracy
Model
k
10
GLN
52.5
74.7
81.2
87.9
TB
GraphRetro
53.7
68.3
72.2
75.5
LocalRetro
52.6
76.0
84.4
90.6
RetroGFN
49.2
73.3
81.1
88.0
SCROP
43.7
60.0
65.2
68.7
G2G
48.9
67.6
72.5
75.5
Aug. Transformer
48.3
73.4
77.4
DualTF aug
53.6
70.7
74.6
77.0
MEGAN
48.0
70.9
78.1
85.4
Tied Transformer
47.1
67.1
73.1
76.3
TF
GTA
51.1
67.0
74.8
81.6
Graph2SMILES
52.9
68.5
70.0
75.2
Retroformer
52.9
68.2
72.5
76.4
Chimera
59.6
82.8
89.2
94.2
RetroBridge
50.8
74.1
80.6
85.6
RETRO PRODFLOW
50.0
0.15
74.3
0.30
81.2 + 0.08
85.8 + 0.04
RETRO SYNFLOW
60.0 + 0.22
77.9 + 0.13
82.7 + 0.15
85.3 + 0.19
Our main results are presented in Table 1, comparing RETRO SYNFLOW to several previous SOTA models for retrosynthesis on top-k accuracy, and Table compares RETRO PRODFLOW-RS on top-k round-trip accuracy. In particular, RETRO PRODFI.OW-B OWWRRS achieves top-1 accuracy of 60%, approx- imately 20 % higher than other template-free methods that do not perform ensembling. outperforms RetroBridge and RETRO PRODFLOW, which use source distribution of product molecules to build the Markov Bridge/CTMC 2, This demonstrates that synthons encode valuable structural information and serve more informative source of distribution for generating reactants. Additionally, RETRO SYNFLOW also produces notable gains in top-3 and top-5 exact match accuracy. Our approach is competitive with Chimera [27], framework that ensembles many different retrosynthesis models across both graph and SMILE string- based methods. As result, our method is complementary to their framework and may be less resource intensive.
Table 2: Top-k Round-trip coverage and accuracy USPTO-50k test dataset.
Model
Round- Trip Coverage
Round-Thi Trip Accuracy
k=1
k=
k=5
k=10
k=1
k=3
k=ū
k=
10
GLN
82.5
92.0
94.0
82.5
TB
LocalRetro
82.1
92.3
94.7
82.1
71.0
66.7
RetroGFN
76.7
69.1
65.5
60.8
MEGAN
78.1
88.6
91.3
78.1
67.3
61.7
Graph2SMILFE
76.7
56.0
46.4
TF
Retroformet
78.6
71.8
67.1
RetroBridge
85.1
95.7
97.1
97.7
85.1
73.6
67.8
56.3
RETRO SYNFLOW-RS
88.9
97.3
98.4
98.9
88.9
73.9
69.1
61.8
RETRO PRODFLOW-RS
91.4
97.6
98.7
99.3
91.4
84.1
80.1
75.1
Chimera [27] an ensemble method combining multiple retrosynthesis models (TF + TB) Retrooridge was evaluated using T' 500 sampling steps done in Igashov et al. [18]
7

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However, exact match accuracy is limited because it does not capture the fact that multiple reactants (some which may not exist in the dataset) can synthesize the same product molecule. Therefore. we evaluate RETRO PRODFL.Ww- RS on round-trip accuracy and compare it to the baselines present [18] in addition to [13]. shown Table 2, RETRO PRODFFLOW-RS is capable of generating diverse and feasible reactants, outperforming state-of-the-art methods on round-trip coverage and accuracy. Additionally, RETRO SYNFLOW-RS also achieves competitive results on round-trip coverage and accuracy.
10.27
X0.16
X0.14
X0.1
X0.1
RetroBridge
B
0.41
0
00.11
X0.06
/0.05
Product
RETRO PRODFLOW
C
0.44
70.22
00.17
00.08
0.003
RETRO PRODFLOW-RS
D
0.58
0..26
/0.06
0.05
00.01
RETRO SYNFLOW
Figure 4: Top-5 reactants selected by each method. A star indicates an exact match, checkmark indicates round-trip match but not an exact match, and cross means neither.
4.3
Ablation Studies
In Table we show the performance of RETRO PRODFLOW-RS for the synthons reactants generation task. Here, top-k accuracy refers to the proportion of synthons where the true set of reactants exists in the top-k predicted sets of reactants. Furthermore, we evaluate the effects of providing the product molecule as additional context the flow matching model po-
Table 3: Top-k accuracy for synthon completion task on USPTO-50K test dataset.
Model
3
5
10
RSF (w/o product)
59.7
75.5
79.1
82.0
RSF (w product)
67.7
82.9
85.7
87.5
Non.
(a) Exact match accuracy
(b) Round trip accuracy
(c) Coverage
k-5
10
Figure 5: Performance of RETRO PRODFL.OW-R on the USPTO-50K validation set as we vary the number of particles for SMC resampling. We sample N = 50 reactants per product.
In Figure 5, we study the performance of RETRO PRODFL.W--B OS- by varying the number of particles the SMC resampling procedure for FK-steering. We find that =4 particles already provides significant gains in round-trip accuracy and coverage with negligible reduction in exact match

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accuracy. As we increase the number of particles further, we see noticeable increases in round-trij accuracy and coverage with a slight decrease in exact match accuracy.
Table 4: Comparing RETRO PRODFLOW- RS against baselines on the USPTO-500 test set.
Round-Trit Trip Coverage
Round- -rip Accuracy
Model
h:
1
k =
3
k
5
k == 10
k =
1
k
3
k ==
5
k
10
RPF
84.4
95.3
96.9
97.7
84.4
72.8
66.8
57.6
RPF-RS
91.4
97.6
98.7
99.3
91.4
84.1
80.1
75.1
Greedy Sampling
89.4
97.0
98.4
99.2
89.4
79.0
73.0
66.2
RPF (400 reactants)
84,2
95.3
97.1
98.1
84.2
73.1
68.5
60.7
Next, we further demonstrate the benefits of reward-based steering with SMC resampling by compar- ing RETRO PRODFLOW-RS against two baselines. The greedy sampling approach does not perform any steering or resampling and instead selects the particle with the highest reward the end of the generation process. Table 4 shows that RETRO PRODFLOW-RE RSS outperforms greedy sampling, SMC resampling. Furthermore, we also scale the computational budget of RETRO PRODFLOW by generating N = 400 reactants per product molecule. This has the same computational cost as RETRO PRODFLOW-RS which uses K particles and samples 100 reactants per product molecule. Again, Table shows that increasing the number of reactants sampled per product does not improve round-trip accuracy and coverage by a significant amount, highlighting the need for reward- based SMC steering. Additional ablation studies are available in Appendix B.
highlighting the superiority
5 Related Works
Template-based. The approaches for single-step retrosynthesis can be divided into two main cate- gories: template-bas based and remplate-free. Reaction templates molecular subgraph patterns that code pre-defined reaction rules to transform a target product into simpler reactants. They can be hand- crafted by experts [15, 47] or extracted algorithmically from large databases [5]. The main challenge for template-based methods, such as Segler and Waller [36], Coley et al. [5]. Dai et al. [9], is ranking and selecting the correct templates for target molecule. More recently, Gaiński et al. [13] utilizes the recent GFlowNet framework to build RetroGFN, model capable of composing existing reaction tem- plates to explore the solution space reactants beyond the dataset increase fcasibility and diversity.
Templates provide a strong inductive bias, and template-based methods offer greater interpretability the expense of generalization. On the other hand, template-free approaches directly transform products to reactants without pre-defined rules, providing more flexibility. Many works in this area frame the task as eqquence-to-sqquencc modelling problem on SMILES string representation of molecules [24, 57, 46, 48]. Another approach is to use graph representation of molecules and transform product molecule graphs to reactant graphs [33, 39]. The recent work by Igashov et al. [18] builds a Markov Bridge model between the space of products and reactants. Also, Laabid et al. [22] employs absorbing state diffusion and builds graph diffusion model to generate reactants. Other works leverage combination of graph-based and SMILE representations of molccules [38, 49, 52]. In the context of multi-step retrosynthesis, prior works have used forward-synthest model to select for promising reactants [7, 57].
Template-free.
Discrete Diffusion and FM. Discrete diffusion and flow matching are powerful class of generative models that have demonstrated impressive results across various tasks, e.g. language modelling [28], symmetric group learning [56], and biological applications such as protein synthesis[1, 17]. 3D molecule generation [10, 42], and DNA sequence design [44]. Traditionally, continuous-state diffusion models have also been employed for discrete data generation tasks such as graph synthesis [55, 53, 19, 29], despite relying on hard-coded post-processing steps.
6 Conclusion
In this work, we approached single-step retrosynthesis as learning the transport map between two intractable distributions and explored two different options for the source. We first introduced RETRO

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PRODFLOW, discrete flow matching model that transforms products into reactants. Next, we proposed RETRO SYNFLOW, the first flow matching model that transforms products reactants through intermediary structures known synthons. We demonstrated that synthons serve more informative source of distribution for generating reactants with RETRO SYNFL.OW achieving 60% top- 1 accuracy, beating previous SOTA methods for retrosynthesis modelling. Furthermore, we enhanced the diversity and feasibility of predictions by leveraging Feynman- Kac steering, an inference-tinee reward-based steering method. We define the reward function using forward-synthesis model motivated by round-trip accuracy. A reward-steered version of RETRO PRODFLOW achieves 80.1% top-5 round-trip aceuracy, beating template-fre free SOTA methods by up to 19%.
Although template-ber based planning constrained, may remain preferable to many chemists due to its alignment with established reactants, reagents, and reaction types. Future work could explore incorporating template information to guide the generation process toward specific compound sets, enhancing the interpretability of our method.
7
Acknowledgements
This work was funded, in part, by the NSERC DG Grant (No, RGPIN-2022-04630), the Vector Institute for AI, Canada CIFAR AI Chairs, Google Gift Fund, and the CIFAR Pan-Canadian Al Strategy through a Catalyst award. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through the Digital Research Alliance of Canada alliance. can. ca, and companies sponsoring the Vector Institute www. vectorinstitute. ai/ #partners, and Advanced Research Computing at the University of British Columbia. Additional hardware support was provided by John R. Evans Leaders Fund CFI grant. AJB is partially supported by an NSERC Postdoc fellowship and supported by the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI Hub No. EP/Y028872/L. QY is supported by UBC Four Year Doctoral Fellowships
10

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Supplementary Materials
Table of Contents
Experimental details
16
A.1 Neural Network Model
16
A.2 Training
17
A.3
Additional Features
17
Additional Ablation Studies
18
B.1 Synthon Prediction
18
B.2
Sampling Steps
18
B.3
Inference Time Comparison
19
C
Round-trip Visualization
19
Additional Sampling Scheme
Predictions Visualization
21
A Experimental details
In this section, we elaborate on our experimental setup. We view a set of molecules as a single potentially disconnected graph. We follow the procedure outlined in Igashov et [18] and introduce a "dummy" node as an atom type. The graph of reactant molecules has at least as many nodes as the product molecule graph. During training and inference for RETRO PRODFLOW, we append dummy nodes to each product molecule. This covers 99.4% of the reactions in the USPTO-50K test dataset. Following Igashov al. [18], the remaining reactions are removed from the data. During inference, these dummy nodes are potentially transformed into true atom nodes. Similarly, we also append ten dummy nodes the synthon molecule graphs when using RETRO SYNFLOW. There are 16 atom types (not including dummy atoms) and 4 bond types (not including no bond). Our methods are implemented PyTorch [30], and we also use an open-source software RDKit [23], for operations involving chemical reactions and molecular graphs.
A.1 Neural Network Model
We use graph transformer network [1] also used by Igashov et al. [18] to model the denoiser Pe of the flow matching process. The denoiser model takes a noisy graph (v, EE) and graph-level features y input and outputs probabilities of graphs PRODFLOW, the product molecule graph is also provided as input to the denoiser model. This done by appending the product molecule graph's 's node feature vector and adjacency matrix to the node feature vector and adjacency matrix of the noisy graph. For RETRO SYNFLOW, both the product molecule graph and synthon molecule graph provided input.
the data distribution. In the case of RETRO
The graph transformer network similar to the standard transformer architecture and con- sists of graph attention module depicted in Figure 6. The graph attention module takes in input node features v, edge features E, and gevel features y. The FiLM is defined as FiLM(M; M2) M, Wi (M, W2) M2 + M2 where W,, W2 are learnable weights. Also,
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PNA is defined as PNA(v) = cat(max(v), min(v), mean (v)a std(v)) W where W is learnable weight.
F'
1
Lin
Lin
Lin
+
FiLM
FiLM
Flatten
Flatten
PNA
PNA
Lin
E
y
Softmax
Lin
1
E
Scaling
Outer Product
Lin
Lin
2
num byers
Figure 6: An overview of the graph attention module used in the graph transformer network. The output features passed through normalization layer and fully connected layer the end.
A.2 Training
Our training runs are done on either an NVIDIA RTX 3090 (24 GB of memory) or V100 (32 GB of memory). We train all of our models to 600 epochs which can take to 32 hours. We compute top-k accuracy metrics on a portion of the validation set every fixed number epochs and select checkpoint that has the highest top-1 accuracy. The models trained using batch size of 32. We use AdamW [26] with a learning rate 0.0002.
A.3 Additional Features
We utilize the additional features proposed by [51] and used in Igashov et al. [18] input to models. We briefly state these features here for completeness.
Message Passing Neural Networks cannot detect graph cycles, so we add them as features using formulas up to cycles size We compute node-level features (how many cycles does this node belong to) up to size 5 and graph-level features (how many cycles does this graph have) up to size 6. Fortunately, we can use formulas to compute the graph-level features y, and node-level features X,, which can be efficiently computed the GPU. In the following formulas, denotes the
Cycles.
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vector containing node degrees and ||-1| denotes the Frobenius norm:
X3 diag( A 1/2
X4 (diag(A*) d(d
X, = (diag(A²) 2 diag( )
A((diag( ([(diag(A))]3 (1)) diag(A A)) /2
Y3 X 1n/3
y4 X 1n/4
Y5 X 1n/5
Y6 Tr(A 3TT(A² A²) A A
-diiia (A²). diag( (A 4)) 6Tr(A*) - 4Tr(A²)
+ +4T A + 3 A*|| 12 & A²) + 4T(AA)).
Spectral Features. We compute graph-lewe level features: the number of connected components (which the multiplicity of the eigenvalue), and the first non-zero eigenvalues of the graph Laplacian. We also compute node-level features: estimate of the biggest connected component and the first two eigenvectors associated with the first two non-zero eigenvalues. Since molecular graphs in USPTO-50k have fewer than 100 nodes, the computation of these spectral features is not concern.
B
Additional Ablation Studies
B.1 Synthon Prediction
In this section, we provide some additional ablation studies examining the performance of our methods. In Table 5, we evaluate performance of RETRO PRODFLOW-RS when sampling N = 100 reactants with M = synthon predictions. We vary N1, the number of reactant predictions generated for the highest ranking synthon prediction from the reaction center identification model. We verify that need to generate more reactants for the highest-scoring synthon prediction obtain competitive top-k accuracy.
Top-k Accuracy
N₂
k =
3
k =
k
10
90
58.2
77.4
81.9
84.4
80
58.3
78.0
82.3
84.7
70
58.1
77.5
82.0
84.6
60
56.6
77.0
81.6
84.5
50
48.5
76.1
81.2
84.2
Table 5: Top-k accuracy (exact match) on the USPTO-50k validation dataset of RETRO SYNFLOW with M synthon predictions, sampling N 100 reactants and varying the split. Given Ni. have N₂ 100 N1.
B.2 Sampling Steps
Next, conduct study to understand how the number of sampling steps affects the performance of flow matching compared to RetroBridge. We find that T' 50 sampling steps sufficient for RETRO PRODFLOW obtain SOTA results. Although RetroBridge achieves higher accuracy at 5 T 10 sampling steps compared to flow matching, both methods fail to reach competitive performance. At T 50 steps, RETRO PRODFLOW achieves
improvement
RetroBridge.
also investigate the inference time required by RETRO SYNFLOW to sample with T 50 sampling steps. We run this test RTX 3090 with 24 GB of memory. The average time required to sample 100 reactants for each product molecule USPTO-50K test dataset 5.46 + 2.95 seconds.
100 reactants
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100
Accuracy
Accuracy
60-
60
40
40
20
60
80
100
20
40
60
80
100
Sampling Steps
Sampling Steps
(a) RETRO PRODFLOW
(b) RetroBridge
k=1
k-3
k-5
k-10
Figure 7: The performance of RETRO PRODFLOW and RetroBridge as vary the number of sampling steps.
B.3 Inference Time Comparison
In this section, provide ablation comparing the inference time scaling of RSF with the number of particles against other template-based and template-frre models. Our model has 3.38 million parameters. We benchmark on an RTX 3090 with 24 GB of memory.
Method
Mean time
GLN
0.295
LecalRetro
0.022
RetroBridge
56.3
RSF (K=1)
5.4
RSF (K=2)
15.6
RSF (K=4)
30.6
RSF (K=6)
45.7
RSF (K=8)
53.9
Table 6: Average inference time (seconds) to sample 100 reactants for given product in the USPTO- 50k test set.
We note that 30 seconds for sampling a set of 100 reactants for a given product is a completely feasible time for applications of models like In particular, this speed does not prevent the use of our method as a component of multistep retrosynthesis planning pipeline. Additionally, the sampling time for RetroBridge with T=500 steps and 100 reactants 50 seconds.
C Round-trip Visualization
This section provides short study analyzing the outputs of RETRO PRODFL.Ow and RETRO PRODFLOWw-SS with when applying inference-ting time steering guide generations towards outputs that optimize round-trip accuracy. We look at the top-1 accuracy results on the USPTO-50k test dataset for simplicity. Our main finding from this ablation that it still possible for RETRO PRODFLOW generate reactants that are incorrect, i.e., do not match the true reactants and are not feasible, i.e., the forward synthesis model prediction does not match the ground-trute product. Table 7 shows how steering based on round -rip reward affects the incorrect/correct prediction made by RETRO PRODFLOW. In total, 257 correct examples in the test dataset get converted to incorrect examples when applying reward steering. On the other hand, 251 incorrect examples are converted correct examples when applying steering. As we increase the number of particles, i.e., increase the strength of steering, this gap widens. This results in overall decrease in exact-match accuracy as we force reactants towards
2 particles. We aim to understand how top-k accuracy can decrease
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more diverse and feasible predictions, Figures 8 and 9 show the visualizations between the outputs of RETRO PRODFLOW and RETRO PRODFLOW-RS The predicted product column refers the prediction of the forward-synthesis model given the predicted reactants as input.
Table 7: Top-1 predicted reactants from RPF and RPF-RS quantified into four categories. The round-trip match column indicates whether the prediction made by RPF-RS is round-trip match.
RPF
RPF-RS Round-Trip Match
Count
Percentage
Correct
Incorrect
T
225
4.5
Correct
Incorrect
F
32
0.64
Incorrect
Correct
4.5
Incorrect
Correct
F
25
0.50
Correct
Correct
T
1848
36.9
Correct
Correct
F
388
7.7
Incorrect
Incorrect
1810
36.1
Incorrect
Incorrect
453
9.0
Predictions
Product
True Reactants
RETRO PRODFLOW
RETRO PRODFLOW WSR
Figure 8: A visualization of reactions where RETRO PRODFLOW-RS generates an incorrect reactant prediction that is still feasible, while the RETRO PRODFLOW generates the correct reactant prediction. There are 225 examples in the test set that correspond to this case.
Predicted Product
Predictions
Product
True Reactants
RETRO PRODFLow
RETRO PRODFLOw-RS
Figure 9; A visualization of reactions where RETRO PRODFLOW-RS generates an incorrect reactant prediction that is infeasible. RETRO PRODFLOW generates the correct reactant prediction. There are 32 examples the test that correspond to this case.
To obtain a better understanding of the errors the forward-synthesis model, we evaluate the top-k accuracy of Molecular Transformer the USPTO-50K dataset. Furthermore, find that for the
20

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non-steering outputs, 25% of the chemically valid reactants were misscored by the forward-synthesi model. For the Ft-steered outputs, only 14% of the chemically valid reactants were misscored by the forward-synthesis model.
Top-1
Top-3
Top-5
Top-10
75.0
82.0
83.0
83.0
Table 8: Top-k accuracy of Molecular Transformer on the USPTO-50K evaluation set.
D Additional Sampling Scheme
Recently, there has been increasing interest in developing advanced adaptive sampling schemes for discrete diffusion and flow matching models [16, 32, 21, 31J. These developments aim to reduce errors in the generation process while improving inference speed. As explained in Section 2.1, update the intermediate sample Xr using the following transition kernel, X Cat(x)+4i )(]]) - which is analogous to the Euler update step in continuous flow matching. Inspired by this analogy, we explore a higher-order sampling scheme [32] based on the Runge-Kutta (RK) method for solving ODEs. The update step for the method is as follows:
8ith Cat($ 5(x)) + (81 Xt))
(3)
1 x Cat X Xithi 0(x)) + (ug x1) + hhuitn (
1
(4)
This update step requires two model evaluations from pe instead of one. Table 9 compares the performance of RETRO PRODFLOW using this sampling scheme with 25 steps against RETRO PRODFLOW using the Euler-inspired sampling scheme with 50 steps.
Table 9: Top-k accuracy of RPF on the USPTO-50k test set sampling N = 50 reactants per product.
Model
10
49.6
73.3
79.6
83.6
RPF (RK 25 steps)
49.3
71.2
76.4
80.0
RPF (RK 50 steps)
49.3
72.5
78.6
82.3
E Predictions Visualization
We provide some additional visualizations of the generated reactants from our methods. In the following figures, "E" represents exact-match between the prediction reactant and an "R" indicates a round-trip match but not an exact-match. We show the top-3 reactants.
21

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Product
Predictions
0,20c
0.33R
3.MC
0.30
bet
0,77
0.0R
Figure 10: Visualizations of predictions made by RETRO PRODFLOW. Examples are taken from the USPTO-50K test set randomly.
Product
Symthre
Predetion
Reactwrt
622g
204k
Figure 11: Visualizations predictions made by RETRO SYNFLOW. Examples are taken from the USPTO-50K test set randomly.
22

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Product
Predictions
8
gour
2.1
0,07E
0.23
2000
0,02
0418
0.2
0.22
Figure 12: Visualizations of predictions made by RETRO PRODFLOW-RS. Examples are taken from the USPTO-50k test set randomly.
23

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Reviews:
[{"review_id": "W24qf5BK3B", "reviewer": "Reviewer_R1w9", "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.\n\nThis is as far as I know the first use of flow  matching and FK steering for this task. Nice!\n\nThe model achieves good performance.\n\n\nOverall this paper should be accepted to the conference after some comments have been addressed", "questions": "1) which Codebase have the authors used to compute the metrics?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\nInteresting model formulation and results. \nNice use of forward model via FK steering\n\nWeaknesses:\nThe 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\n\nMissing citations to highly relevant work\nhttps://arxiv.org/abs/2405.17656\n\nOther works that have used forward model steering/filtering for synthesis planning:\n- Segler er al Nature 2018 \n- Coley et al Science 2019\n- Schwaller et al Chemical Science 2020  \n\nThe 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.\n\n\nComment:\nThis reviewer will increase score after discussion of above points", "was_revised": true, "final_justification": "updated score based on discussion.\n\none thing to note is that retrobridge code does not perform stereochemically aware assignment, for the final version my recommendation would be to run the metrics through www.github.com/microsoft/syntheseus", "rebuttal": "We would like to thank the reviewer for the time and effort they spent on reviewing our work!  We appreciate that the reviewer found our model formulation and use of the forward model for FK steering to be interesting. We now address the main points raised in the review.\n\n## Ablation without FK steering\n\nWe acknowledge the reviewer's comment that our ablation should include a result that does not make use of FK steering. We believe that there may be a slight confusion, which we now hope to clarify. In our main results, we report our initial model, RetroProdFlow, which directly transports product molecules to their corresponding reactants, and importantly, is devoid of any FK steering. As evidenced by Table 1 results, we find RetroProdFlow to be a performant model on par with previous template-free methods like RetroBridge [6], but short in performance compared to RetroSynFlow, which is our primary model. Consequently, we argue that the baseline that the reviewer requests is already present in the main paper. \n\nIn addition, we further argue that our proposed approach RetroSynFlow, is a combination of flow matching with the distribution of synthons serving as a strong inductive prior and FK steering to facilitate improvements in round-trip accuracy. Therefore, it is natural to consider these two components in one system rather than in isolation. \n\nWe hope that this clarification here fully alleviates the reviewers' valid concern, and we will update the wording in the main paper to highlight that RetroProdFlow does not employ FK steering.\n\n## Citations to related work\n\nWe thank the reviewer for pointing us to these related works. In our updated draft, we will include [1,2,3] in our related works section to discuss the use of a forward-synthesis model to filter reactants in retrosynthesis.  In particular, a key difference between our proposed RetroSynFlow and these related works is the use of a forward-synthesis model to aid in multi-step retrosynthesis planning, while our work pertains to **single-step retrosynthesis prediction**. Consequently, these works do not report results on the USPTO-50k dataset, which we use in our evaluation.  \n\nWith regards to related works [4]  to the related works section and [5] to our baselines. The key differences between our work and [4] are that the denoiser model in [4] requires the atom-mapping as input, while our flow-matching denoiser does not. Furthermore, [4] employs absorbing state diffusion, which assumes that the source distribution is an absorbing state. In stark contrast, discrete flow matching allows us to learn a map between synthons/products and reactants directly. \n\nWith regards to Chimera [5], we argue that this is a framework that ensembles many different retrosynthesis models across both graph and SMILE string-based methods. As a result, our method is complementary to their framework as it can be employed in the ensemble. Additionally, since Chimera ensembles various models, it may potentially be more resource-intensive than our single method in RetroSynFlow.  We update the main table with following new entries:\n\n| Method | Top-1 | Top-3 | Top-5 | Top-10 |\n ---           |   ----   |     ----  |   ----  |  ----- |\n| Chimera[5]  | 59.6 | 82.8 | 89.2 | 94.2 |       \n\n## Questions\n> Which Codebase have the authors used to compute the metrics?\n\nWe used the evaluation code from RetroBridge [6] to compute metrics. \n\n## Concluding remarks\n\nWe thank the reviewer for their time and effort in reviewing our paper. We hope that our rebuttal here answers all the great points raised by the reviewer, allowing them to potentially upgrade their score as they initially suggested. We are also more than happy to answer any further questions that arise in the rebuttal period. Please let us know.\n\n## References\n\n[1] Segler et al. “Planning chemical syntheses with deep neural networks and symbolic AI”, 2018. \n\n[2] Coley et al. “A robotic platform for flow synthesis of organic compounds informed by AI planning”, 2019. \n\n[3] Schwaller et al. “Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy”, 2020.\n\n[4] Laabid et al. “Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models”, 2025.\n\n[5] Maziarz et al. “Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases”, 2024.\n\n[6] Igashov et al. “RetroBridge: Modeling Retrosynthesis with Markov Bridges”. 2024"}, {"review_id": "nGDhWcPTRa", "reviewer": "Reviewer_FqVg", "summary": "The 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.", "questions": "See detailed comments under Weaknesses. Each point can be considered a prompt/question and I am willing to adjust my review if addressed appropriately.", "limitations": "Please consider discussing limitations or avenues for future research at the end of the paper.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "strengths:\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.", "was_revised": true, "final_justification": "The authors have addressed all my concerns. I read all the other reviewers comments on the different rebuttals; I raised the scores.", "rebuttal": "We would like to thank the reviewer for the time and effort they spent engaging with our work. We appreciate that the reviewer finds our application of discrete flow matching to retrosynthesis novel and the use of FK-steering to improve round-trip accuracy a strength of our work. We now address the questions and concerns.\n\n## Template-free vs semi-template \n\n> The authors repeatedly describe their methods, including RSF, as \"template-free\", which is somewhat misleading. I would encourage the authors to consider repositioning the model as a semi-template method.\n\nWe appreciate the reviewer’s constructive feedback on the positioning of our work as a semi-template method. Semi-template approaches aim to combine the interpretability of template-based models with the flexibility and generalization ability of template-free methods. These methods typically operate in two stages: the first stage involves predicting the reaction center to identify synthons, and the second stage, which we focus on, completes the synthons into valid reactants. While some prior works (e.g., [2]) do not explicitly differentiate between semi-template and template-free methods, the consensus in the literature is to classify such two-stage frameworks as semi-template approaches [3]. In light of this, we will revise the introduction and related work sections to clarify the characteristics of semi-template methods and more accurately position our contribution within this framework.\n\n## Using imperfect forward synthesis model as the reward oracle\n\n> The reward oracle for FK-steering is an imperfect forward synthesis model. 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.\n\nThis is indeed a fair point. Forward-synthesis is an easier task than retrosynthesis, and correspondingly, forward models such as Molecular Transformer [1] have strong performance. However, they are still subject to false positives and false negatives, which can potentially bias the steering process towards invalid reactants and limit performance in round-trip accuracy. We will add this to the limitation sections at the end of our paper. \n\nWe evaluate the results of Molecular Transformer on the USPTO-50k dataset and obtain the following results.\n\n|Top-1| Top-3| Top-5 | Top-10|\n|   ---    |   ---   |    ---   |      ---  |\n| 75.0 | 82.0  | 83.0   |   83.0 |\n\nWe refer the reviewer to Appendix C, specifically Table 6 and Figures 8 and 9, for a brief discussion and visualization of the potential bias introduced by the forward-synthesis model. In particular, there are cases where the reward-steered model may generate reactants that are not feasible and do not match the ground truth, whereas the non-steered version produces a correct prediction.\n\nFurthermore, for the non-steering outputs, 25% of the chemically valid reactants were misscored by the forward-model oracle. For the FK-steered outputs, only 14% of the chemically valid reactants were misscored by the oracle.\n\n## Miscellaneous \n> The finding in Table 3 that removing the product context improves synthon completion accuracy is counter-intuitive.\n\nWe apologize for the confusion regarding this typo. Indeed, using the product context increases accuracy, and we have fixed this typo so the columns are labeled correctly. \n\n## Concluding remarks\n\nWe hope that our responses were sufficient in clarifying all the great questions asked by the reviewer. We thank the reviewer again for their time, and we politely encourage the reviewer to consider updating their score if they deem that our responses in this rebuttal, along with the new experiments, merit it.\n\n## References\n\n[1] Schwaller et al. “Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction” 2025. \n\n[2] Igashov et al. “RetroBridge: Modeling Retrosynthesis with Markov Bridges”. 2024\n\n[3] Somnath et al. “Learning Graph Models for Retrosynthesis Prediction”. 2021"}, {"review_id": "jE4CmSt0rd", "reviewer": "Reviewer_5pkR", "summary": "This 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.", "questions": "* 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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "Happy with the author's responses and updated my score.", "rebuttal": "We thank the reviewer for the time and effort spent reading and engaging with our work. We are glad that the reviewer found our work to be a novel application of discrete flow matching, demonstrating strong results with comprehensive evaluations and ablations. We now address the reviewer's questions. \n\n## Novelty of RetroSynFlow \n\nWe acknowledge the reviewers' concern that the core novelty of RetroSynFlow may not be initially apparent. We would like to politely push back against this assertion, as discrete flow matching is not an established approach for single-step retrosynthesis. Consequently, adapting the discrete flow matching framework—whose primary success has been with masked/absorbing states—to a setting where the prior is a set of reactants/synthons is a simple yet effective novel design contribution of our work. Furthermore, to the best of our knowledge, the use of FK steering with a discrete flow matching model operating on synthons is also novel. In particular, our proposed use of the forward-synthesis model as a reward for inference-time steering is a novel insight that is specifically tailored to the single-step retrosynthesis setup. In contrast, prior work like RetroGFN [7]  had only considered reward models **during training**. We argue that these design decisions lead to an overall approach whose combination of components is novel and drives the current empirical success over prior SOTA template free approaches.\n\n## RetroSynFlow vs. template-based approaches\n\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\nWe appreciate the reviewer’s nuanced feedback on the distinction between template-free and template-based approaches. Template-based methods rely on a fixed library of templates extracted from datasets, which encode only the transformation rules observed in those datasets. Thus, these methods introduce a strong inductive bias, highlighted by the strong performance of LocalRetro, which outperforms many template-free baselines. However, this reliance on predefined templates limits the chemical space these methods can explore, making it difficult for them to generalize to unseen reactions [14–16]. For instance, GLN achieves top-1 accuracy of 52.5% on USPTO-50k but drops to 39.5% on USPTO-Full. Also, extracting reaction templates can be expensive, either requiring expert chemists to handcraft rules or relying on computationally intensive subgraph matching algorithms [17]. Automatically extracted template sets may contain errors, as they are often less curated and validated [18]. Finally, we note that RetroSynFlow outperforms existing template-free baselines, further demonstrating its effectiveness.\n\n## Use of round-trip accuracy \n\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\nWe use round-trip accuracy to address the limitations of exact-match accuracy, which overlooks the fact that multiple valid reactant sets can produce the same product but may not appear in the dataset. Top-$k$ round-trip accuracy captures this by measuring the proportion of feasible reactants (via a forward model) among the top-$k$ predictions. Importantly, it can be optimized independently of top-$k$ accuracy. Our reward-steered methods improve round-trip accuracy by generating diverse, feasible reactant sets while maintaining competitive top-$k$ performance by including ground-truth reactants among their outputs. Even without reward steering, our model performs on par with state-of-the-art methods on round-trip metrics. Although not a perfect measure, round-trip accuracy is widely used in the literature [1–3, 7–9, 11].\n\n## Benchmarking on other datasets\n\nWe thank the reviewer for their suggestion on testing on the USPTO Full and other datasets. We believe exploring how our methods scale to datasets such as USPTO Full is an interesting direction for future work. In particular, we follow the standard experimental practice in many prior recent works, including other diffusion-based approaches and synthon-based approaches, which solely report results on the USPTO-50k dataset [1-9]. Furthermore, we have conducted comprehensive ablations on USPTO-50k, and we argue that our results are comprehensive in empirical caliber. Nevertheless, we will include a discussion on these points as part of our conclusion in our updated draft \n\n## Questions\n> What is # of parameters of the model? How does inference time scale with the number of particles? Please provide inference wall time versus template-based baselines, and discuss GPU memory needs for SMC. \n\nThe model has 3.38 million parameters. The following table provides the average inference time to sample 100 reactants for a given product in the USPTO-50k test set. We benchmark on an RTX 3090 with 24 GB of memory.\n\n| Method | Mean time |\n|    ---       |         ---      |\n|   GLN            | 0.295|\n| LocalRetro    | 0.022|\n|RetroBridge   |  56.3 |\n| RSF (K=1)    | 5.4    |\n| RSF (K=2)    | 15. 6 |\n| RSF (K=4)    |  30.6 |\n| RSF (K=6)    |  45.7 |\n| RSF (K=8)    |  53.9 |\n\nWe note that 30 seconds for sampling a set of 100 reactants for a given product is a completely feasible time for applications of models like ours. In particular, this speed does not prevent the use of our method as a component of a multistep retrosynthesis planning pipeline. Additionally, the sampling time for RetroBridge with T=500 steps and 100 reactants is ~50 seconds. \n\n>  Molecular Transformer's errors may bias results but are not quantified.\n\nWe evaluate the results of Molecular Transformer on the USPTO-50k dataset and obtain the following results.\n\n|Top-1| Top-3| Top-5 | Top-10|\n|   ---    |   ---   |    ---   |      ---  |\n| 75.0 | 82.0  | 83.0   |   83.0 |\n\nWe refer the reviewer to Appendix C, specifically Table 6 and Figures 8 and 9, for a brief discussion and visualization of the potential bias introduced by the forward-synthesis model. In particular, there are cases where the reward-steered model may generate reactants that are not feasible and do not match the ground truth, whereas the non-steered version produces a correct prediction.\n\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\nSynthons correspond to the product graph with deleted or modified edges. In the USPTO-50k dataset, the reactions have one edge deleted on average.  Identifying synthons is a key step in how human chemists approach retrosynthetic analysis, as it helps them visualize how a complex molecule can be constructed from simpler, idealized fragments that point to the actual reactants required for synthesis. [12, 13]  Employing synthons provides more information about the reaction center and, along with the product context, simplifies the generation task by allowing the denoiser model to potentially “localize” the changes that need to be made to transform the current noisy graph into the reactant molecule graph.  \n\n> Can you report the proportion of FK steered outputs that are actually chemically valid (validated with RDKit) but misscored by the oracle? A sensitivity analysis on oracle noise would clarify reliability.\n\nFor the non-steering outputs, 25% of the chemically valid reactants were misscored by the oracle. For the FK-steered outputs, only 14% of the chemically valid reactants were misscored by the oracle.\n\n## Concluding remarks\n\nWe hope that our responses were sufficient in clarifying all the great questions asked by the reviewer. We thank the reviewer again for their time, and we politely encourage the reviewer to consider updating their score if they deem that our responses in this rebuttal, along with the new experiments, merit it.\n\n## References\n\n[1] Wan et al. “Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer”, ICML 2022.\n\n[2] Laabid et al. “Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models”, ICLR 2025. \n\n[3] Igashov et al. “RetroBridge”, ICLR 2024\n\n[4] Somnath et al. “Learning Graph Models for Retrosynthesis Prediction” NeurIPS 2021.\n\n[5] Shi et al. “A Graph to Graphs Framework for Retrosynthesis Prediction”, ICML 2021. \n\n[6] Zhong et al. “Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing” 2023. \n\n[7] Gianski et al. “RetroGFN”, 2024. \n\n[8] Chen and Jung. “Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention.” 2021\n\n[9] Qiao et al. “Advancing Retrosynthesis with Retrieval-Augmented Graph Generation”. AAAI 2025. \n\n[10] Schwaller et al. “Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction”. 2019\n\n[11] Zeng et al. “Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment”. 2024\n\n[12] Corey, “General Methods for the Construction of Complex Molecules”. 1967. \n\n[13] Warren, “Designing Organic Syntheses: A Programmed Introduction to the Synthon Approach”. 1978. \n\n[14] Yan et al. “RetroXpert”. 2020\n\n[15] Segler et al. “Modelling Chemical Reasoning to Predict and Invent Reactions”. 2016.\n\n[16] Thakkar et al. “Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain”. 2020\n\n[17] Liu et al. “Retrosynthetic reaction prediction using neural sequence-to-sequence models”. 2017\n\n[18] Yao et al. “Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis. 2024."}, {"review_id": "rsFI4tRdeJ", "reviewer": "Reviewer_L2PP", "summary": "The 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.", "questions": "See Weaknesses", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "We would like to thank the reviewer for the time and effort they spent on reviewing our work. We are glad that the reviewer found our work to be conceptually clean,  technically sound, and our use of synthons to be well motivated.  We now address the main questions raised by the reviewer. \n\n## Benchmarking on other datasets\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\nWe thank the reviewer for their suggestion on testing on the USPTO Full and other datasets. We believe exploring how our methods scale to datasets such as USPTO Full is an interesting direction for future work. In particular, we follow the standard experimental practice in many prior recent works, including other diffusion-based approaches and synthon-based approaches, which solely report retrosynthesis results on the USPTO-50k dataset [2-8]. Furthermore, we have conducted comprehensive ablations on USPTO-50k, and we argue that our results are comprehensive in empirical caliber. Nevertheless, we will include a discussion on these points as part of our conclusion in our updated draft.\n\n## GFlowNet vs Flow Matching\n\n> The claim of being the first “flow‑matching framework for retrosynthesis” would benefit from a clearer differentiation from the recent GFlowNet.\n\nWe appreciate the reviewer’s observation and the opportunity to clarify how our approach differs from GFlowNets. GFlowNets and flow matching are fundamentally distinct frameworks. GFlowNets aim to sample trajectories whose probabilities are proportional to a given reward function. They model how probability mass flows through a directed acyclic graph (DAG) of partial trajectories. In contrast, flow matching models a continuous-time Markov chain that transports mass between arbitrary source and target distributions. Unlike GFlowNets, flow matching does not rely on a reward function to learn this mapping. Given these conceptual differences, we believe it is appropriate to state that our work is the first to apply flow matching to retrosynthesis.\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\nAlthough the two-stage framework relies on identifying synthons, this is not a problem in practice since reaction-center prediction can be performed with high accuracy. An off-the-shelf reaction center prediction model achieves a top-2 and top-3 accuracy of 85.1% and 89.5% respectively.  The more challenging aspect of retrosynthesis is synthon completion [9], which is the main aspect addressed by our work.  \n\n## Compute time Comparison\n\nThe following table provides the average inference time to sample 100 reactants for a given product in the USPTO-50k test set. We benchmark on an RTX 3090 with 24 GB of memory.\n\n| Method | Mean time |\n |   ----      |       ----       |\n|   GLN            | 0.295|\n| LocalRetro    | 0.022|\n| RetroBridge  |  56.0 |\n| RSF (K=1)    | 5.4    |\n| RSF (K=2)    | 15. 6 |\n| RSF (K=4)    |  30.6 |\n| RSF (K=6)    |  45.7 |\n| RSF (K=8)    |  53.9 |\n\nWe note that 30 seconds for sampling a set of 100 reactants for a given product is a completely feasible time for applications of models like ours. In particular, this speed does not prevent the use of our method as a component of a multistep retrosynthesis planning pipeline. Additionally, the sampling time for RetroBridge with T=500 steps and 100 reactants is ~50 seconds. \n\n## Concluding remarks\n\nWe thank the reviewer again for their valuable feedback. We hope that our rebuttal addresses their questions and concerns, and we kindly ask the reviewer to consider a fresher evaluation of our paper if the reviewer is satisfied with our responses. We are also more than happy to answer any further questions that arise. \n\n## References\n\n[1] Dai et al. “Retrosynthesis Prediction with Conditional Graph Logic Network” Neurips 2019.\n\n[2] Laabid et al. “Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models”, ICLR 2025. \n\n[3] Igashov et al. “RetroBridge: Modeling Retrosynthesis with Markov Bridges”, ICLR 2024\n[4] Somnath et al. “Learning Graph Models for Retrosynthesis Prediction” NeurIPS 2021.\n\n[5] Shi et al. “A Graph to Graphs Framework for Retrosynthesis Prediction”, ICML 2021. \n\n[6] Zhong et al. “Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing”  2023. \n\n[7] Gianski et al. “RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets”, 2024. \n\n[8] Chen and Jung. “Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention.” 2021\n\n[9] Smith. “Organic Synthesis”, 2017."}]

Author rebuttal:

Meta-review: The paper is the first to apply discrete flow matching to retrosynthetic prediction.

The model achieves a strong performance on the USPTO50k benchmark. LocalRetro outperforms the method for larger K. The comparison on roundtrip shows state-of-the-art performance, but not all method used forward model based fine-tuning (which is part of the metric).

Experiments were conducted on USPTO50k, the standard testbed for the subfield, but not fully realistic or representative of practical applications. It remains to be seen how the method scales to larger datasets.

Three reviewers recommended acceptance, while one recommended rejection of the work.

The impact of the work is at the moment not fully clear, given the limited experimental scope and non-state-of-the-art performance compared to methods such as LocalRetro. However, all in all, it is a solid demonstration of the applicability of discrete flows to retrosynthetic prediction, which warrants acceptance.

Last but not least, I would like to ask politely the Authors to clarify in the abstract the statement "Empirically, we demonstrate RSF achieves 60.0% top-1 accuracy, which outperforms the previous SOTA by 20%”, which doesn’t seem to be the case given Table 1.
Decision: Accept (poster)