Exploring Synthesizable Chemical Space with Iterative Pathway Refinements
Authors: Seul Lee
Karsten Kreis
Srimukh Prasad Veccham
Meng Liu
Danny Reidenbach
Saee Gopal Paliwal
Weili Nie
Arash Vahdat
Venue: ICLR 2026 Oral (2026)
Keywords: drug discovery
molecule generation
synthesizable molecule design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effectively navigate exponentially large combinatorial space of synthesizable molecules and suffer from poor coverage. To address this problem, we introduce ReaSyn, an iterative generative pathway refinement framework that obtains synthesizable analogs to input molecules by projecting them onto synthesizable space. Specifically, we propose a simple synthetic pathway representation that allows for generating pathways in both bottom-up and top-down traversal of synthetic trees. We design ReaSyn so that both bottom-up and top-down pathways can be sampled with a single unified autoregressive model. ReaSyn can thus iteratively refine subtrees of generated synthetic trees in a bidirectional manner. Further, we introduce a discrete flow model that refines the generated pathway at the entire pathway level with edit operations: insertion, deletion, and substitution. The iterative refinement cycle of (1) bottom-up decoding, (2) top-down decoding, and (3) holistic editing constitutes a powerful pathway reasoning strategy, allowing the model to explore the vast space of synthesizable molecules. Experimentally, ReaSyn achieves the highest reconstruction rate and pathway diversity in synthesizable molecule reconstruction and the highest optimization performance in synthesizable goal-directed molecular optimization, and significantly outperforms previous synthesizable projection methods in synthesizable hit expansion. These results highlight ReaSyn's superior ability to navigate combinatorially-large synthesizable chemical space.

Published as a conference paper at ICLR 2026
EXPLORING SYNTHESIZABLE CHEMICAL SPACE WITH ITERATIVE PATHWAY REFINEMENTS
Seul Lee" KAIST
seul. www.leeetttst.c..oo
Karsten Kreis & Srimukh Prasad Veccham & Meng Liu & Danny Reidenbach & Sace Paliwal & Weili Niei & Arash Vahdat
NVIDIA
Ikkreis, sveccham, menliu, dreidenbach, saeep, wnie, avahdat faaviiia.com
ABSTRACT
A well-known pitfall of molecular generative models is that they are not guar- anteed generate synthesizable molecules. Existing solutions for this problem often struggle to effectively navigate exponentially large combinatorial space of synthesizable molecules and suffer from poor coverage. To address this prob- lem, we introduce ReaSyn, an iterative generative pathway refinement frame- work that obtains synthesizable analogs to input molecules by projecting them onto synthesizable space. Specifically, representation that allows for generating pathways in both bottom-up and top- down traversal of synthetic trees. We design ReaSyn so that both bottom-up up and top-down pathways can be sampled with a single unified autoregressive model. ReaSyn thus iteratively refine subtrees of generated synthetic trees in bidi- rectional manner. Further, we introduce a discrete flow model that refines the generated pathway at the entire pathway level with edit operations: insertion, dele- tion, and substitution. The iterative refinement cycle of (1) bottom-up decoding, (2) top-down decoding, and (3) holistic editing constitutes powerful pathway reasoning strategy, allowing the model to explore the vast space of synthesiz- able molecules. Experimentally, ReaSyn achieves the highest reconstruction rate and pathway diversity in synthesizable molecule reconstruction and the highest optimization performance in synthesizable goal-directed molecular optimization, and significantly outperforms previous synthesizable projection methods in syn- thesizable hit expansion. These results highlight ReaSyn's 's superior ability to navigate combinatorially- synthesizable chemical space. Our code is available ReaSyn.
propose simple synthetic pathway
at httpps:///i thub. huub. com/WWIDDA-Diiitaaa
Reconstruction rate (%)
INTRODUCTION
300
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Diversity (Building Douildingbbooll block)
Discovering molecules with desired properties in the chemical space comprises the core of drug discovery. However, drug dis- covery pipelines are challenging and labor-intensive due to their vast design space and multi-objective nature. Molecular gener- ative models have recently emerged notable breakthrough with the potential to greatly accelerate the drug discovery process. However, their practical impact has remained limited as they often suffer from a common shortcoming: generated drug candidates are often synthetically inaccessible (Segler et al., 2018b; Gao Coley, 2020; Walters & Barzilay, 2021).
18.0
manne Sunni
1l0.588
0.161
0.624
0.958
0,020
ReaSyn (ouns)
Synformer
SynNet
Diversity (Pathway)
Figure 1: Synthesizable molccule re- construction on test molecules con- structed from ZINC250k. Full results provided in Table 1.
"ork during an internship at NVIDIA.
1 Equul advising.

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(8)
(b)
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RXNB
Iterative refinemmnt
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Buttom up
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Edit Bridge
Bottom-up
RXN2
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Bottorthup tree generation
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Figure 2: (a) Bottom-up and top-down traversal of a synthetic tree. (b) Overall framework of ReaSyn. ReaSyn's generation cycle consists of three steps. First, an initial synthetic tree is generated by the autoregressive model in bottom-up direction. Next, the autoregressive model repredicts randomly selected subtree in top-down direction. Finally, the Edit Flow model refines the generated tree in holistic manner. This process can be repeated multiple times, and the best pathway that yields a product molecule of the highest similarity to the given target molecule is selected as the final solution. The sampling processes of the autoregressive model (the first and the second steps) and the Edit Bridge model are depicted in Figure 3(a) and Figure 3(b), respectively.
This problem arises from neglecting synthesizability during multi-objective molecular optimization, which often leads to exploration outside the synthesizable space. Although this issue can be tackled by incorporating a synthesizability score Schuffenhauer, 2009; Coley et al., 2018; Thakkar et al., 2021), this approach is largely impractical since the heuristic scores are often poor proxies. This is because synthesizability is complex function of molecular structure, and existing proxies cannot take available building block stocks or reaction selectivity into account (Gao & Coley, 2020).
additional optimization objective (Ertl &
An alternative solution is to constrain the design space to synthetically accessible space by generating synthetic pathways instead of solely generating final product molecules. This can be obtained either by generating synthesizable molecules in a de novo way (Bradshaw et al., 2020; Swanson et al., 2024; Koziarski al., 2024; Cretu et al., 2025; Seo al., 2025) by projecting (possibly unsynthesizable) input molecules into the synthesizable space (Gao et al., 2021; Luo et al., 2024; Gao et al., 2025; Sun al., 2025). In the latter approach, often called synthesizable projection or analog generation, a model learns to correct unsynthesizable molecules by generating pathways that lead to synthesizable analogs with similar structures. In contrast to the former approach, synthesizable projection benefits from versatile and modular design and can be used with any off-the-shelf molecule generation method. can also be applied to explore neighborhoods in the synthesizable space, allowing the model to per- form different molecule optimization tasks, including hit expansion or lead optimization. Given these benefits, in this paper, we aim to solve the synthesizable projection problem using generative models.
When generating synthetic pathways in an autoregressive setting, a key decision is the direction in which the tree-structured pathway is generated (Figure 2(a)). Bottom-up generation (Bradshaw et al., 2020; Gao al., 2021; Swanson al., 2024; Luo et al., 2024; Gao al., 2025; Koziarski et al., 2024; Cretu et al., 2025; Seo et al., 2025) and top-down generation (Sun et al., 2025) each have distinct advantages: the former can start from valid building blocks, and the latter aligns with the chemist's intuition. In this paper, we argue that identifying synthetic pathways for a given molecule is search problem that benefits from unifying the two approaches: model must navigate an exponentially large combinatorial space of possible intermediates and reactions, reasoning over sequences of chemical transformations that yield analogs. To this end, we introduce ReaSyn', unified framework that can generate synthetic pathways both bottom-up and top-down (Figure 2(b) and Figure 3). This bidirec- tional design allows us to initialize set of synthetic pathways given an input molecule and iteratively refine them in both directions. This iteration is required changes a node must be propagated both upward in the tree (to update the remaining synthetic pathway compatible with the new intermediate product) and downward in the tree (to regenerate a subtree that would yield the updated product).
The upward or downward sampling given a synthetic tree can be considered as edit operations that refine trees partially in one direction. To take these refinement operations one step further, also introduce a tree editing scheme based on Edit Flow (Havasi et al., 2025) which we term as Edir Bridge (Figure 3(b)). Specifically, Edit Bridge is novel discrete flow over pathway sequences that further refines the given pathway generated by the autoregressive model through edit operations: insertion, deletion, and substitution. This in turn allows ReaSyn to jointly edit both the tree skeleton
Inspired by the reasoning nature of our problem, ReaSyn is pronounced "reason".
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(a)
f
Incerton
Bottom-up
Transformer Encocer
Transformer Decoder
Subustraoon
Inout molccule
Input moleculc
Edit Operation Predictor
Pro
Edit Bridge
Transformr Encodor
Transformer Decoder
Top-down
Transtomerr
Frooder
Transformet Decoder
601
BB2
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Figure 3: ReaSyn adopts an encoder-decoder Transformer architecture. After the encoder encodes the input molecule, the decoder predicts the synthetic pathways of its synthesizable analogs. [START] and [KND] tokens are omitted for simplicity. (a) Bidirectional synthetic pathway generation of ReaSyn, ReaSyn's autoregressive model predicts the synthetic pathways in the sequential representation. ReaSyn's training and inference scheme tailored for the bidirectional synthetic pathway generation enables to designate a specific sampling direction using a single autoregressive model. (b) Holistic pathway editing of ReaSyn. ReaSyn's 's Edit Bridge model takes the full pathway generated by the autoregressive model and jointly edits the tree skeleton and semantics.
and semantics at the entire pathway level. To the best of our knowledge, this paper is the first to bridge between learned and data distributions via editing operations.
Generative modeling over synthetic pathways requires choosing a pathway representation. Prior works (Bradshaw et al., 2020; Gao et al., 2021; Swanson et al., 2024; Luo et al., 2024; Gao et al., 2025; Koziarski et al., 2024; Cretu et al., 2025; Seo et al., 2025; Sun et al., 2025) have used hierarchical representation, including node type (i.e., reaction vs. building block) and node features (i.e., reaction class or building block features like Morgan fingerprints (Morgan, 1965). Striving for simplicity, ReaSyn introduces simple sequential representation for traversing synthetic trees. ReaSyn climinates (I) information loss in molecular fingerprints by directly using SMILES to represent building blocks and (2) error accumulation and architectural complexity arising from the hierarchical representations. Using this representation, ReaSyn adopts a novel training and inference scheme that can enforce a specific traversal direction in single encoder-decoder Transformer (Vaswani et al., 2017).
We experimentally validate the effectiveness and versatility of ReaSyn on various tasks including synthesizable molecule reconstruction, synthesizable goal-directed molecular optimization, and synthesizable hit expansion. ReaSyn outperforms existing methods with superior reconstruction rates and pathway diversity in synthesizable molecule reconstruction, and achieves vate-of-the-an performance in finding synthesizable chemical optima in synthesizable goal- saalddieetedd molecular optimization and hit expansion. For example, as shown in Figure 1, ReaSyn significantly outperforms existing methods in exploring the synthetic chemical space to reconstruct synthesizable molecules (87.9% vs. 18.0% reconstruction rate) with greater sampling diversity (0.658 vs. 0.181 building block diversity). All these results indicate that ReaSyn has broader coverage of the synthesizable chemical space, highlighting its efficacy as a practical tool in real-world drug discovery scenarios. We summarize contributions follows:
We introduce simple synthetic pathway representation that enables traversal of synthesis trees both bottom-up and top-down directions.
We propose new bidirectional search strategy in synthetic pathway space that unifies bottom-up and top-down sampling.
We propose Edit Bridge, novel discrete flow that bridges between the learned distribution in generative model and the data distribution through edit operations.
We propose ReaSyn, framework that integrates bottom-up decoding, top-down decoding, and holistic editing to establish multi-view pathway generation method for synthesizable projection.
We demonstrate the effectiveness and versatility of ReaSyn in various synthesizable molecule generation and optimization tasks through extensive experiments.
2
RELATED WORK
Synthesizable molecule design. Molecular synthesizability is vital problem in drug discovery and therefore has received a lot attention. In synthesizable molecule design, the synthesizable chemical space is defined by sets of reaction nules and building blocks, and the design space is constrained to this
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space. Various algorithms have been proposed to navigate the space to find synthesizable drug candi- dates, including autoencoders (AEs) (Bradshaw et al., 2020), variational autoencoders (VAEs) (Pedawi et al., 2022), Bayesian optimization (Korovina et al., 2020), genetic algorithms (GAs) (Gao et al., 2021), Monte Carlo tree search (MCTS) (Swanson et al., 2024), and GFlowNets (Koziarski et al., 2024; Seo et al., 2025; Cretu al., 2025). However, most of them are either only capable of con- sidering single-step synthetic pathways or require extensive oracle calls to perform goal- goal-directed molecular optimization. Furthermore, these de novo generation methods directly generate molecules in the synthesizable space exhibit poor explorability and optimization performance against the target chemical properties. An alternative approach projecting given molecules into the synthesizable space to suggest synthesizable analogs (Gao et al., 2021; Luo et al., 2024; Gao et al., 2025; Sun et al., 2025). However, these methods are unable to reliably suggest synthetic pathways that reconstruct syn- thesizable molecules. All of these results indicate a lack of explorability in the synthesizable chemical space, largely due to their insufficient reasoning capability on the generated synthetic pathways.
Retrosynthesis planning. A related area is retrosynthesis planning, which aims to predict synthetic pathways of given synthesizable molecules in a top-down direction (Coley et al., 2019; Genheden et al., 2020; Kim et al., 2024; Sathyanarayana et al., 2025). Yu et al. (2024) addressed another problem. double-ended starting material-constrained synthesis planning, with bidirectional node expansion in AND-OR graph search. Retrosynthesis planning methods, however, cannot suggest synthesizable analogs given unsynthesizable molecules, because the target molecule is invariable starting point for the search. In addition, since they are typically a combination of a single-step reaction prediction model and a search algorithm, they cannot consider the entire pathway to optimize the end products for target chemical property. In contrast, ReaSyn solves the problem of synthesizable projection that can be considered as loose single-ended synthesis planning. The target molecule guides synthesis rather than being strict constraint, allowing flexible handling of the target molecule regardless of its syn- thesizability and enabling optimization of the entire pathway based on the end molecule's 's properties.
3 BACKGROUND
Problem definition. A set of building blocks B and set of reactions R together define a synthe- sizable chemical spuce. A reaction R E R is a function that maps reactants to a product, and the synthesizable space is defined as the set of product molecules that can be formed by the iterative appli- cation of reactions on compatible combinations of building blocks in B. A reaction rule is described by SMILES Arbitrary Target Specification (SMARTS) (Daylight Chemical Information Systems, 2019), a regular expression-like representation for Simplified Molecular Input Line Entry System (SMILES) (Weininger, 1988) patterns of reactants and products. A reaction step is defined by convert- ing the matching pattern in the reactant SMILES to a specified pattern in the product SMILES (e.g.. using RDKit (Landrum et al., 2016)), and the synthetic pathway p is defined these reaction steps. Given the target molecule æ as input, the goal of synthesizable projection is to generate the pathway p that produces the end product molecule that similar to x:
specific ordering of
p' argmax sim(prod(p).. .)),
where sim is the molecular similarity and prod is the function that gets the end product by executing
Edit Flow (EF) for sequence generation. Recently, Havasi et al. (2025) proposed a new discrete flow model to overcome the fixed-length problem of discrete diffusion models while preserving Markov Chain
their advantage of processing full sequences. Edit Flow defines a Continuous- (CTMC) (Campbell et al., 2024) at the full sequence level rather than at the token level. The CTMC transports sequences from a source (e.g., noise) distribution p(p) to a target (c.g., data) distribution q(p) via edit operations: token insertions, deletions, and substitutions. Its CTMC transition is defined as edit rates, uf (ins(p, i, a,))))) ug (del(p, i) lp), and uf (sub(p, i, a)(p) for the insertion, deletion, and substitution operations, respectively, where denotes the model parameters. Here, is the token index where the operations are performed, and a is the token to be inserted or substituted.
The distribution of source (po) and target (pi) sequence pairs is called coupling π r(poPP)). whose marginals are p and g, i.e Epo (Po. PI) = q(Pi) and Σp. T P.) = p(po). Edit Flow uses the empty coupling where Pa is an empty sequence or the uniform coupling where p(po) is uniform over
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tokens. Another important component in Edit Flow training is alignment, which is the process of align- ing Po and P1 to identify a set of edit operations that transform Pu to P1- Given the aligned (po. P1) pairs, Edit Flow can be trained using Bregman divergence loss. More details are in Section B.
4 METHOD
The traverse direction is the key decision in generation of tree-structured synthetic pathways (Fig- ure 3(a)). Most existing synthetic pathway generation methods adopt bottom-up (BU) generation, which starts with building blocks and progressively predicts more complex molecules toward the target product molecule. Although the BU approach guarantees starting from valid building blocks in B, it inevitably faces harder problem than top-down (TD) generation because (1) it needs to search in B rather than R during the most challenging initial few steps, where B >> [R| (e.g.. 211,220 vs. 115), and (2) is easier to reason backward from the given target molecule. However, although TD solves an easier task and is in line with how chemists infer pathways, it lacks the guarantee of reaching legitimate building blocks included in B at leaf nodes, making it less popular choice in existing synthetic pathway generation methods. The distinct characteristics of the two approaches highlight the need for a new bidirectional framework that can complement and integrate them.
To this end, we introduce ReaSyn, synthesizable projection framework that integrates BU decoding, TD decoding, and holistic editing. We start with introducing the bidirectional pathway representation of ReaSyn in Section 4.1. Next, we describe the bidirectional iterative cycle of ReaSyn in Section 4.2. Finally, we describe the holistic refinement scheme ReaSyn using Edit Bridge in Section 4.3.
4.1 REPRESENTING SYNTHETIC PATHWAYS IN BOTTOM-UP AND TOP-DOWN DIRECTIONS
We first introduce new sequential representation that can represent synthetic pathways in both BU and TD directions. Prior works (Luo et al., 2024; Gao et al., 2025) represent a synthetic pathway using post-order traversal of its corresponding synthetic tree (Figure 7(a)) and autoregressively gener- ate p instead of directly generating the SMILES of the product molecule (Segler et al., 11)). Unlike in the previous notation, synthetic trees are traversed in both post-order and reverse post-order in ReaSyn's 's notation. While post-order traversal yields the BU pathway representation (i.c., from leaves to root), the reverse post-order yields the TD pathway representation (i.e., from root to leaves) by re- versing the post-order-travennt quence PBu consisting of B blocks (i.e., subsequences), where each block b E (1..... B) represents either a molecule or a reaction, is represented as Pau := p @ @.... + $ Here, denotes the con- catenation operation and p¹ denotes the b-th block of p. The TD sequence the same pathway PTD is then represented as PID := n + 2- + ① p'. Molecular blocks are represented by SMILES with delimiter tokens indicating the start and end of the block ( ([MOL: START] J and [MOL: ),
sequence (Figure 3(a) and Figure 7(b)). Specifically, BU pathway se-
while reaction blocks consist of single token indicating the reaction type. These complementary sequences, PBU and PTD, use a single unified token vocabulary for molecular and reaction blocks. More details the pathway representation and examples of PBU and PTD are provided Section C.I.
Not only can the new notation represent pathways in both directions, but it also resolves drawbacks of pathway representations in existing methods. First, existing representations use molecular fingerprints to represent building blocks to handle large B, but since the mapping between a molecule and its fingerprints is not bijective, there exists information loss processing building blocks. In addition. molecular fingerprints are sparse and a mistake in single entry in the fingerprints can result in significantly different molecule. Our notation eliminates these problems by directly representing building blocks using SMILES, which is a smoother representation than molecular fingerprints. Secondly, existing methods employ hierarchical representations that first determine the node/token type (i.e., building block or reaction) and then predict specific node/token features (i.e., building block fingerprints or the reaction class). This scheme is prone to error accumulation and requires additional architectural complexity. For example, Luo al. (2024) uses separate classifier heads for token type, reaction, and building block fingerprints, and Gao et al. (2025) uses a Bernoulli diffusion head for building block fingerprints. Our notation circumvents these problems by using a unified vocabulary.
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4.2 REASONING SYNTHETIC PATHWAYS IN BOTTOM-UP AND TOP-DOWN DIRECTIONS
Bidirectional training and inference. Based on the developed notations PAU and PTD. we propose simple yet effective training and inference scheme that does not require additional computational costs. Specifically. we adopt an encoder-decoder Transformer (Vaswani et al., 2017) where the encoder encodes æ: and the decoder aims to autoregressively generate p. The dataset consists of (z. p) pairs, and we randomly switch between p Pnu and p PrD with probability of 0.5 during training to equip a single autoregressive model with the ability to handle both directions. The model is trained with the next token prediction loss, Importantly, since molecular blocks consist of multiple SMILES tokens while reaction blocks (and [START]/C [END]) consist of single token, we introduce a loss weighting scheme based on token type:
C
E (m.p)-- prr PID)
To lo P1:-11
+
To x. P15-1) (2)
[mool]
Lother
where Imol and Lother are the sets of token indices of molecular blocks and token indices of other blocks, respectively, and p; denotes the i-th token of p. This balances the learning of synthesis pathways in accordance with the number of building blocks and reactions in them. During inference, we introduce a simple scheme to control the direction. We first note that pBu starts with the [ token whereas PTD starts with a reaction token. To enforce particular BU TD direction, we simply bias the categorical distribution for the first token to sample from [MOL: START] ] for BU and a reaction for TD sequences. The bias is simply enforced by setting the log probabilities (c.g.. logits) of non-reaction tokens to -oo. This simple bidirectional training/inference scheme allows ReaSyn to perform bidirec- tional sampling with a single model while achieving performance comparable to two standard unidi- rectional models (Table 6 and Table 7). Training and inference details are included in Section D and E.
Reasoning with bidirectional iterative cycles. Based on the bidirectional model capable of generating pathways in both directions, we propose to complement and integrate BU and TD approaches through an iterative cycle that alternates between the two sampling directions (Figure 2). The iterative approach is designed to walk through the vast synthesizable space to discover analogs. Given the target product molecule, the cycle starts with generating an initial synthetic pathway PBU of B blocks in a BU direction. Next, a block index randomly drawn from {l,..., B-1), and starting from the b-th block, ReaSyn predicts the right-hand subsequence of its complementary sequence pid
<(B S(B-b) again in TD direction. This allows the generated synthetic tree to be refined at the subtree level. This cycle can be run multiple times until a pathway is found for a molecule sufficiently similar to the input target molecule. Searching in the synthetic tree space is challenging because nodes in the tree are interconnected with each other, and the bidirectional iterative cycle ensures that updates made at any node in the tree propagate properly all the way the root and leaf nodes.
PBU P
4.3 REFINING SYNTHETIC PATHWAYS IN HOLISTIC VIEW WITH EDIT BRIDGE
The bidirectional iterative cycle that combines BU and TD generation enables deepened reasoning on synthetic pathways. ReaSyn takes this test-time search strategy one step further with Edit Flow (Havasi et al., 2025). Edit Flow suggests edits at the entire sequence level transport the sequence toward the target distribution via edit operations (Eq. (4)). Taking full advantage of its holistic and flex- ible nature, we propose adding a step the cycle that provides another perspective on the generation.
We propose Edit Bridge, an extension of Edit Flow that forms a bridge from pretrained distribution to- wards the data distribution. Recall that Edit Flows build couplings from the source sequence Po the target sequence P1. In the original Edit Flows, Po is assumed to be an empty or uniformly random se- quence which has no or minimally random overlap with P1- Instead, in this paper, we form a coupling between sample generated by our bidirectional autoregressive model (Section 4.2) and the target se- quence pt. Specifically, we assume that po is generated via the aforementioned generative cycle the autoregressive model and train Edit Flow to generate the target P: using coupling formed between the two. We term this model Edit Bridge as it bridges between the autoregressive model and data distribu- tion. As shown in Table 8, our Edit Bridge coupling shows much higher Po-P1 alignment rate than the empty or uniform couplings (70.6% vs. 0.0% or 2.6%). Consequently, it requires much less edit opera- tions convert Po P1 than the two couplings, resulting in much less sampling steps during inference

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Table 1: Synthesizable molecule reconstruction results. The results are the means and the standard deviations of 3 runs, The best results are highlighted in bold.
Dataset
Method
Reconstruction rate (%)
Similarity
Div. (Pathway)
Div. (BB)
Enamine
SynNet (Gao al., 2021)
25.2 0.1
0,661 + 0.000
0.014 +: 0.001
0.239
0..02
SynFormer (Gao al., 2025)
66.3 = 0.6
0,913 + 0.001
0.101 = 0.001
0.587 0.002
ReaSyn (ours)
95.0 + 0.0
0.987 + 0.001
0.118 + 0.002
0.753
0.004
ChEMBL
SynNet (Gao al., 2021)
7.9 =
0.542 0.000
0.009 0.000
0.090
0.001
SynFormer (Gao al., 2025)
19.7 + 0.4
0,668 + 0.002
0.039 0.000
0.192
0.002
ReaSyn (ours)
31.7 + 0.3
0.751 + 0.001
0,050 + 0.000
0.321
0.002
ZINCIK
SynNet (Gao al., 2021)
12.6
0.456 0.001
0.001
0.000
0.089
0.000
SynFormer (Gao ul., 2025)
18.0 +: 1.2
0.624 +: 0.003
0.020
0.000
0.181
0.001
ReaSyn (ours)
87.9 0.2
0,958 1 0.003
0,071 0.001
0.658 0.002
Table 2: Synthesizable molecule reconstruction results with the ChEMBL test set in Luo al. (2024). The
results of SynNet (Gao et al., 2021) and ChemProjector (Luo et al., 2024) are taken from Luo al. (2024).
Method
Reconstruction rate (%)
Sim. (Morgan)
Sim. (Scaffold)
Sim. (Gobbi)
SynNet (Gao et ul., 2021)
5,4
0.427
0.417
0.268
ChemProjector (Luo et al., 2024) SynFormer (Gao et al., 2025)
13.4
0.616
0.603
0.564
19.5 + 0.2
0.698 0.002
0.673 + 0.002
0.643 + 0.003
ReaSyn (ours)
33,0 + 0.2
0.762 0.002
0.754 + 0.001
0.722 + 0.001
(30.0 vs. 94.6 or 142.9). In our paper, Edit Bridge is implemented using the same encoder-decoder Transformer (Vaswani al., 2017) architecture the autoregressive model, with additional heads to predict the edit operation rates u.( (ins(p, i, a) pp), uf (del(p, i))p), and u, (sub(p, é, a)lp) (Eq. (4)).
Since both (1) the sampling process of Pu and (2) the alignment process that computes the edit oper- ations converting Po to P1 are computationally expensive, we prepare 10.5M training data (i.e., (Po. p1) p1) pairs and their align operations) offline. Further details are included in Section D. Unlike autore- gressive generation, Edit Bridge allows ReaSyn to holistically consider the entire synthetic pathway. As shown in Figure and Figure 9, Edit Bridge plays a significant role in refining pathways by jointly editing the tree skeleton and semantics. We summarize ReaSyn's single iterative refinement cycle consisting of (1) bottom-up decoding, (2) top-down decoding, and (3) holistic editing in Algorithm 1.
5 EXPERIMENTS
Following Gao et al. (2025), we adopt the set of I15 reactions that include common uni-, bi- and tri-molecular reactions, and the of 211,220 purchasable building blocks in the Enamine's U.S. stock catalog retrieved in October 2023 (Enamine, 2023), together covering a synthesizable chemical space broader than 1060 molecules. The details the model architecture are included in Section C.2.
5.1 SYNTHESIZABLE MOLECULE RECONSTRUCTION
Setup. Even given vast synthesizable space, previous synthesizable molecule generation methods have struggled to cover this space extensively. We evaluate the coverage of ReaSyn in the synthesizable chemical space with synthesizable molecule reconstruction, where the goal is to reconstruct given molecules by proposing synthetic pathways. Following Gao et al. (2025), the reconstruction evaluated on randomly selected 1,000 molecules from the Enamine REAL diversity set (Enamine, 2023) and ChEMBL (Gaulton et al., 2012). In addition, to simulate common world scenarios in which the set of purchasable building blocks expands after the model is trained, include another challenging benchmark by expanding the building block set with unseen building blocks. Concretely,
add 37.386 molecules with fewer than 18 heavy atoms from ZINC250k (Irwin et al., 2012), yielding a total of 248,606 building blocks. 1,000 molecules generated using the defined reaction rules and the new building blocks are used as the test set, which we denote ZINCIK We also experiment with another test set of ChEMBL molecules introduced by Luo et al. (2024). Further details are included in Section E.
Metrics. Following Gao et al. (2025), reconstruction rate, the fraction of generated pathways that yield the identical product molecule to the input molecule, and similarity, the average Tanimoto similarity the Morgan fingerprint (Morgan, 1965: Rogers & Hahn, 2010) between the generated molecule and the input molecule, are used to evaluate the model. We further report diversity (pathway), the average molecular diversity of end products from analogous pathways, and diversity
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BU
TD
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EF
BU+TD
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SynFormer
Enai
ChEMBL
ZINC750K
Reconstructin rate (9)
95
Reconstruction rate (9%
Reconstouccion rate (9%)
90-
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4000
3000
2000
3000
Time/mrol (s)
Time/moll (s)
Time/mol (s)
Figure 4: Ablation study on synthesizable molecule reconstruction. SynFormer uses the beam width of 96, exhaustiveness of 256, and multiple cycles.
(BB), the average diversity of unique building blocks in analogous pathways, to assess diversity of generation. Here, analogous pathways are defined as pathways that yield product molccule with a Tanimoto similarity 2 0.8 to the input molecule. For the experiment with the test set of Luo al. (2024), similarity between the input target molecule and the projected analog is measured using the Tanimoto similarity on three molecular fingerprints: (1) Morgan fingerprint (Morgan, 1965), (2) Morgan fingerprint Murcko scaffold, and (3) Gobbi pharmacophore fingerprint (Gobbi & Poppinger, 1998), following Luo et al. (2024).
Results. In Table and Table 2, we compare ReaSyn with state-of-the-ar synthesizable analog generation methods, SynNet (Gao et al., 2021) and SynFormer (Gao et al., 2025). ReaSyn significantly outperforms the baselines across all metrics. Specifically, ReaSyn shows high reconstruction rate and similarity, demonstrating that it successfully reconstructs input molecules by extensively exploring synthesizable chemical space. It also shows high diversity in Table I, demonstrating its ability to propose diverse pathways. Note that unlike the Enamine and ZINCIk test sets, molecules in the ChEMBL test set may lie beyond the defined synthesizable space (i.e., may require reactions building blocks beyond B and R), so the performance is generally low on ChEMBL. Notably, ReaSyn outperforms the prior works by particularly large margin on the ZINCIK test set, which simulates challenging scenarios with out-of-distributio test molecules and unseen building blocks. This result highlights the strong generalizability of ReaSyn in generating -uiot-ddistribttinn synthetic pathways.
5.2 ABLATION STUDY
To examine the effect of each component ReaSyn's 's generative cycle, i.e., (1) BU decoding, (2) TD decoding, and (3) Edit Bridge (EB) editing, we conduct ablation studies on the synthesizable molecule reconstruction task (Section 5. in Figure The cycle is proposed as an effective method for scaling test-time compute, and there is generally trade-off between reconstruction rate and sampling time. BU, TD, and EF are ReaSyns that each use only one component of the cycle. Note that since the EB coupling requires pretrained source distribution, EF instead uses the empty coupling. BU+TD is ReaSyn that uses the bidirectional iteration the cycle, and BU+TD+EB is the complete ReaSyn that leverages all the three components. First, comparing BU+TD to BU or TD, we observe that using the autoregressive model a bidirectional way shows much better performance than unidirectional sampling. The unidirectional schemes fail reconstruct large portion of the test molecules even when the test-time compute is increased, demonstrating the importance of the proposed bidirectional iteration. Secondly, we can reconfirm the effectiveness of the bidirectional iteration when we compare BU+TD+EB to EF. Lastly. we observe significant improvements with the additional refinement step using EB, when we compare BU+TD+EB to BU+TD. Overall, these results together highlight the importance of leveraging multiple perspectives in synthetic pathway generation.
5.3 SYNTHESIZARLE GOAL-DIRECTED MOLECULAR OPTIMIZATION
While goal-directed molecular optimization methods aim to address essential drug discovery tasks, their practicality is limited as the generated molecules are often not synthesizable (Gao & Coley, 2020). To overcome this problem, synthesizable projection can be applied to goal-directed molecular optimization. Similar to Gao et al. (2025), we demonstrate the application of ReaSyn in exploring the local synthesizable space in conjunction with off-the-shelf molecular optimization method. Specifically. we use synthesizable projection of ReaSyn as an additional mutation operator of a genetic algorithm (GA) by projecting the offspring molecules after every reproduction step of Graph
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Table 3: Synthesizable goal-directed molecular optimization results on the TDC oracles. The results are the means of AUC top-10 and average top- 10 SA scores of 3 runs. The results for the baselines other than Graph GA-SF and Graph GA taken from Sun al. (2025). The best synthesis-based results highlighted in bold.
Method
Graph GAARReSSy Graph GA-SF SynthesisNet SynNet DoG-Gen
DoG-AF Graph GA
Synthesis
v
X
amlodipine_mpr mpo
0.620
0.696
0.608
0.567
0.537
0.509
0.651
rroooiii_rdiscovery
0.810
0,559
0.582
0.443
0.466
0.357
0.682
drd2
0.977
0.972
0,960
0.969
0.949
0.944
0.970
fexofenadine_mpg mpo
0.788
0.786
0.791
0.764
0.697
0.681
0.785
gsk3b
0.889
0.803
0,848
0.790
0.832
0.602
0.838
jnk3
0.695
0.658
0.639
0.631
0.596
0.470
0.693
medianl
0.274
0.308
0.305
0.219
0.218
0.172
0.261
median2
0.259
0.258
0,257
0.237
0.213
0.183
0.257
osimertinib mpo
0.823
0.816
0.797
0.776
0.751
0,829
perindopril_mng mpo
0.561
0,530
0.524
0.559
0.475
0.433
0,533
ranolazine_mp __p
0.752
0.751
0.741
0.743
0.712
0.690
0.745
sitagliptin_mpo zaleplon_my mpo
0.314
0.338
0.313
0.026
0.048
0.010
0.524
0.460
0.478
0.528
0.341
0.123
0.050
0.458
Average score
0.633
0.612
0.608
0.545
0.511
0.450
0,633
GA (Jensen, 2019), thus ensuring all the resulting offspring molecules lie in the synthesizable space. We denote the resulting GA as Graph GA-ReaSyn. We emphasize that this approach is universal and other molecular optimization methods can also be used. Further details are included Section E.2.
5.3.1 OPTIMIZATION OF TDC ORACLES
Setup. Following Sun et al. (2025), we conduct 15 goal goal-directed molecular optimization tasks of the benchmark of Gao et al. (2022) that simulate real-world drug discovery with the TDC oracle functions (Brown et al., 2019; Huang et al., 2021), and use the AUC top-10 to assess the optimization performance.
Results. The results are shown in Table 3. We compare Graph GA-ReaSyn with synthesis-basee baselines that restrict the generation to the synthesizable chemical space. We also include Graph GA (Jensen, 2019) to examine the impact of the synthesizable projection. Graph GA-ReaSyn outper- forms all saaleeis--bsse baselines in optimization performance, showing its effectiveness discover- ing chemical optima in the synthesizable space. Notably, it achieves comparable optimization perfor- mance to Graph GA even with the synthesizabilityy constraint, validating that the proposed synthesiz- able projection can generate synthesizable analogs while maintaining the core molecular properties.
5.3.2 OPTIMIZATION OF SEH BINDING AFFINITY
Setup. Following Cretu et al. (2025), we conduct optimization of the binding affinity against the protein target soluble epoxide hydrolase (sEH), measured by a pretrained proxy model (Bengio et al., 2021). As the evaluation metrics, the average sEH binding affinity, synthetic accessibility (SA) score (Ertl & Schuffenhauer, 2009), quantitative estimate of drug- drug-likeness (QED) (Bickerton al., 2012), and the AiZyuthFinder success rate (Genheden et al., 2020) of generated molecules are reported. AiZynthFinder is a widely used retrosynthesis planning method, and its success rate is considered a more reliable synthesizability proxy than SA score.
Results. The results shown in Table 4. FragGFN (Bengio et al., 2021) and SynFlowNet (Cretu et al., 2025) are GFlowNets (Bengio et al., 2021) with a fragment action space and a reaction action space, respectively, and SyntheMol (Swanson al., 2024) is a property predictor-guided MCTS method. '(SA)' or 'QED)' denote using a modified optimization objective with SA score or QED. All baselines except FragGFN take the approach of constraining the design space to the synthesizable space. As shown in the table, FragGFN shows a very poor SA score because it does not consider synthesizability. ReaSyn outperforms previous methods in terms of all the metrics, verifying that syn- thesizable projection using ReaSyn is more effective strategy to find synthesizable chemical optima.
5.4 SYNTHESIZABLE HIT EXPANSION
Setup. Synthesizable projection of ReaSyn can suggest multiple synthesizable analogs for a given target molecule with beam search, and thus can be applied to hit expansion to find synthesizable

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Table 4: Synthesizable goal-directed molecular optimization results on the sEM proxy. The results are the means and the
Table 5: JNK3 hit expansion results. The results are the means and the standard deviations of 3 runs. The best results are highlighted in bold.
standard deviations of runs. The results for the baselines taken
from Cretu et al. (2025). The best results are highlighted in bold.
Method
Syn.
sEH
SA
QED
AiZynth.
FragGEN
0.77 = 0.01
6.28
= 0,02
0.30 0.01
0.00
Method
Analog rate (9))
Improve rate (9h
Success
FragGEN (SA)
0.70
0.01
0.09
0.00
rate (5)
SylhhMMol
0.64 0.01
3.08
0.01
0.0110000
0.82
SynNet
13.7 0.1
4 1.0 +
SynFlowNet
0.92 + 0.01
2.92
0.59 0.01
0.65
Syn (SA)
0.94 0.01
0.88
0.93
SynFormer
31.4
SynFlow/ee (QED)
0.03
4.02
0.26 0.74
0.55
Graph GA ReaSyn
0.96 0.00
2.05
= 0.01
0.75 2 0.01
0.97 0.01
ReaSyn (ours)
75.7 11.8 8.
1.0
SynFermer
Better
1.0
ReaSyn lours)
Hit molecule
Synthesizable analogs
0.8
0.8
Simtarky
Singariy
JNK3 scom 0.68
PWK3s U. jim- 0am0 0.500
PVC3scome 0.72 0.72/sim 0094
0.6
0.66
0.4
0.4
0.4
0.4
0.8
JWK
Figure 6: Examples of generated synthesizable analogs from JNK3 hit expansion. Modified substructures in the analogs are indicated hy red.
Figure 5: The distribution of JNK3 scores and
analog similarity of SynFormer and ReaSyn.
analogs of hit molecules. Following Gao et al. (2025), we conduct hit expansion on the identified hit molecules for c-Jun NH2-terminal kinase 3 (JNK3) inhibition. Specifically, the proxy from the TDC library (Huang et al., 2021) is used as the oracle function that scores the inhibition of JNK3, and the top-10 10 scoring molecule from ZINC250K (Irwin et al., 2012) are selected the hits. The JNK3 proxy is set as the reward model to guide the search and 100 analogs are generated for each hit, yielding total of 1,000 synthesizable molecules. Analog rate, the fraction of generated unique analogs that have Tanimoto similarities 2 0.6 to the input hit, improve rate, the fraction of generated unique analogs that have higher JNK3 scores than the original hit, and success rate, the fraction of generated unique analogs that satisfy both, are computed by averaging over the generated molecules.
Results. The results are shown in Table 5. ReaSyn exhibits superior performance the previous synthesizable projection methods in terms of all the metrics. The high analog rate shows that ReaSyn can broadly search the synthesizable space to suggest close analogs. while the high improve rate shows that ReaSyn can find chemical optima in terms of the target property thanks to the goal-directed search. We also provide the distribution of suggested molecules in Figure and examples Figure 6. Compared to SynFormer, ReaSyn generates synthesizable molecules with high similarities and high JNK3 inhibition scores, highlighting its effectiveness in hit expansion.
CONCLUSION
major weakness of molecular generative models the generation of synthetically inaccessible molecules. In our paper, we introduced ReaSyn, an effective framework for synthesizable projection with deepened reasoning on synthetic pathways. The iterative refinement cycle of (1) bottom-up decoding, (2) top-down decoding, and (3) holistic editing constitutes powerful pathway reasoning strategy from multiple viewpoints. ReaSyn showed superior performance on a variety of constrained drug discovery tasks. These results demonstrate ReaSyn's 's strong applicability synthesizable chemical space in real-world drug discovery.
synthesis-
tool to navigate combinatorially--en Considering higher-level compatibility in ReaSyn, such as selectivity functional groups, is left as future work. To facilitate research in this space, we will release code and models publicly.
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Appendix
A ITERATIVE REFINEMENT CYCLE OF REASYN
Algorithm A Single Generation Cycle of ReaSyn
Input: Trained bidirectional autoregressive model MBUID, trained Edit Bridge model MEB. the input target molecule æ, the number of pathways to refine with Edit Bridge NEB
Set P + 0
Bottom-up generation (refinement)
Set PnU.sunn <- [[START], [MOL: START]] with beam search
Generate P&u by sampling and appending tokens to stu, using MBUTD given
Set - U (Psu)
end beam search
Calculate scores of p E P as sim(prod(p), x) (Eqq. (6))
Top-down refinement
with beam search
Sample p from based on their scores
Get PID. the TD representation of Randomly draw a block index b from the block indices of PrD
Repredict Più by sampling using MBUTD given
(if pTD [[START]] then the non-reaction logits to -00)
Set P + U (Pr)}
end beam search
Calculate scores of p E P as sim (pmod(((p)) .)) (Eq. (6))
Edit Bridge refinement
for 1....... NEB do
Sample p from P based on their scores
Generate Pив by refining p using MEB Set P + {PEB)
end for
Calculate scores of p E P as sim sim (prod(p), x)) (Eq. (6))
Output: Generated pathways P and their similarity scores with
We summarize a single refinement cycle of ReaSyn in Algorithm 1.
B OVERVIEW OF EDIT FLOW
Recently, Edit Flow (Havasi et al., 2025), has been proposed to overcome the fixed-variable length problem of discrete diffusion models in sequence generation. By defining discrete flow on sequences, Edit Flow enjoys flexible and ceatiion-relaivee sequence generation. Specifically, Edit Flow defines Continuous- Time Markov Chain (CTMC) (Campbell et al., 2024) at the full sequence level rather than at the token level.
CTMC transports sequences from a source (e.g., noise) distribution p(p) to target (e.g., data) distribution q(p), and this transition is characterized by rate u4- The distribution of source (po) and target (p,) sequence pairs is called the coupling π (po, Pi) distribution, whose marginals are and q2:
Σ T(po.P:) = q((p)) Po
Σ T Pı) = p(po).
P1
Edit Flow in the original paper uses the empty coupling where Po is empty sequence or the uniform coupling where p(po) is uniform over tokens. These couplings are independent couplings,
A sequence is denoted x in the original paper, but instead use to maintain consistent notation with the synthetic pathway sequences.
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Le., (Po. P1) p(po)g(P1), and the source sequence Pu does not contain information about the target sequence P1.
Edit Flow models the transition via edit operations: token insertions, deletions, and substitutions. The CTMC rate uf, where denotes the model parameters, is defined as follows:
uf (ins(p, i, a)pp) Xe. QI)
for i E (1.....,((p)
u (del(p: i)(p) (p)
for i [l,.... (p)}
"n (sub(p, i, a)lp) = X,, Qi, (alp)
for i E [[,...,,([p}}
where is sequence of length n(p) and ins i, a), del(p, i). and sub(p, i, a) denote the insertion, substitution, and deletion operations, respectively. is the token index where the operations are performed, and a is the token to be inserted or substituted. Xς,; 2 0 are the total rates of inserting, deleting, or substituting any token at i. Qt.i are the distributions over tokens given insertion or substitution occurs at position i.
There exist multiple possible sets edit operations that transition from Po to Pl. To handle this, Edit Flow introduces auxiliary sequence that additionally has special blank token The process of identifying the set edit operations using x is called the alignment process. Given the pair (Po. Pt). the aligned pair is denoted as (zo, x1). where 2g and x have the same length. As an example, if (Po- Pt) is ('kitten', 'smitten'), )) the optimal (zo, 21 is ('keitten', 'smitten') ) Edit operations can be easily recovered given the aligned pair as the token conversion deletion if b = e, or substitution if & ε and b b
b is an insertion if e,
Given the aligned pairs, Edit Flow can be trained using Bregman divergence loss:
hs Σ (af 1-x, Kt
((0)=
E TI Z1) P. (PL. =1)
Σ PpP.
(plp))
log ut (p(z,,,,,)
(5)
where p(z,, =1) the operation that substitutes the i-th token 2e with zf and then removes all in ze, which corresponds to one edit operation of Eq. (4).
For more detailed explanation on Edit Flow, please refer to the original paper (Havasi et al., 2025).
C DETAILS ON PATHWAY REPRESENTATION AND MODEL.
C.I BIDIRECTIONAL PATHWAY NOTATION
Prior works (Luo et al., 2024; Gao et al., 2025) represent a synthetic pathway p using post-order traversal of its corresponding synthetic tree (Figure 7(a)). This representation captures synthesis as bi-level sequence consists of the token type level and the token feature level. At the token type level, each token is one of four types: [START], [BB] (building block), [RXN] (reaction), [END]. At the token feature level, different embedding strategies are used based on the token types: [BB] tokens embedded using Morgan fingerprints since the set of purchasable building blocks is large and depends on purchasable stock catalogs. while [RXN] and other fixed tokens use the standard lookup embeddings. To match this bi-level representation, inference performed hierarchically: classifier first predicts the token type, then depending on the outcome, either a fingerprint network predicts the building block or classifier selects the reaction. This representation can describe both linear and convergent synthetic pathways, and has been shown effective in bottom-up synthesis planning (Luo et al., 2024; Gao et al., 2025), especially due to its compatibility with autoregressive generation.
However, this notation and other previous notations on synthetic pathways (1) use molecular finger- prints to represent building blocks handle large B and (2) are hierarchical (Bradshaw et al., 2020; Gao et al., 2021; Swanson al., 2024; Luo al., 2024; Gao et al., 2025; Koziarski et al., 2024; Cretu et al., 2025; Sco et al., 2025; Sun et al., 2025). These characteristics have several drawbacks, therefore have proposed a new representation that directly uses SMILES, is mon-hierarc and is able to represent pathways in both bottom-ur up and top-down directions (Section 4.1).
Our bidirectional notation is parsed using unified token vocabulary of size 272, including [START], [END], [MOL: START], [MOL: ENDJ, 153 SMILES tokens and 1 115 reaction tokens. The SMILES tokens include the following:
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RXNB
Token Vype classification
Bottom-up repr eniattoon
donsification
RY0N2
Top
Top-down representation
BB fingerprnnt prediction
RO011
883
B61
Step 1
Single-step disssfication
Pou
6B
RXN
B6
RXN
RXN
BB1 SMILES
SMILFS
RXN1
B83 SMILES
RXN3
Pro
661 [TNARRO0 1110A
B62 II
RXNI
B63 SIRRRRA
RXN2
RXON3
RKNB
RXN2
A63 6MITES
RXNI
B87 STMITES
BB1
SMITER
(a) Postfix notation
(b) Our notation
Figure 7: Comparison of the postfix notation and our notation. [START] and [END] tokens omitted for simplicity. The postfix notation (Luo 2024; Gao al., 2025) represents synthetic pathway with bi-level sequence consists of token type level the token feature level (i.e., the Morgan fingerprints for [EE] tokens and the reaction class for [RXN] tokens). Consequently, the synthetic pathways are embedded and generated in hierarchical vocabulary without hicrarchy to represent the complementary sequences Pau and PID.
in each autoregressive step. In contrast, our proposed notation uses a unified
H, He, Li, Be, B, C, N, D, F, Ne, Na, Mg, A1, 51, P, 5, C1, Ar,
K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br,
Kr, Rb, Sr, Y, Zr, No, Mo, Ic, Ru, Rh, Pd, Ag, Cd, In, Sn, Sb, Te,
I, Xe, Cs, Ba, La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm,
Yb, Lu, Hf, Ta, W, Re, 0s, Ir, Pt, Au, Hg, T1, Pb, Bi, Po, At, Rn,
Fr, Ra, Ac, Th, Pa, U, Np, Pu, Am, Cm, Bk, Cf, Es, Fm, Md, No, Lr,
RE, Do, Sg, Bh, Hs, Mt, DS, Rg, Cn, Nh, F1, Mc, LV, Ts, 0g, b, Cr
n, O, 8, Pr 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1. }, ( ) =, #, t, Y /, :, -1 0, 7, 7,
$,
The input target molecules are also encoded using the above tokens.
We provide examples of in the bidirectional notation. An example synthetic pathway of -- =NCC1 [N-] (C (=0) C (CC (=0) 0) (2Ccco2) M1c1cccc (F) cl (the tar- molecule of Figure 8) is as follows:
PBU = [START] [MOL:START] [N=]=
(N) N1 ccce (F) c1 [MD]: [MOL: O-C1CC (C2C0002)C(-0)100 [MOL:END] [RXN: 44]
[END]
PTD [START] [RXN: 44] [MOL:START] START] [ (N) N1 clcce
(F) cl [MOL:END) [MOL: START] O=C1CC (C2CCC02) [MOL:
[END]
An example synthetic pathway of Ccl aoc (S (=0) (-O) c2cce (C (CO) cCOCC (E) (F) cc2) (OCC2C02) el (the target molecule of Figure 9) is follows:
PBu = [START] [MOL:START] OCC (CCOCC (F) (F) elace (C1) ccl [MOL: END]
[MOL: START] Celece (C1) a (OCC2C02) c1 [MOL:
[RXN: 32] [END]
PID = [START] [RXN: 32] [MOL: OCC (CCOCC (F) (F) F) clece (C1) cel
[MOL: : END] [MOL: START] Celece (C1) (OCC2C02) el [MOL: END]
[END]
Here, special tokens other than SMILES tokens and reaction tokens are displayed gray. Note
that the first token after the [START] token (the first token sampled during inference) is always [MCL: START] and a reaction token Pau and PrD. respectively.
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C.2 MODEL ARCHITECTURE
ReaSyn has two models, one for the bidirectional autoregressive pathway generation and one for the Edit Bridge pathway editing. For both models, we adopt the standard encoder-decoder Transformer architecture, following Gao et al. (2025). Specifically, the encoder has 6 layers, with hidden dimension of 768, 16 attention heads, a feed-forward dimension of 4096, and the maximum sequence length of 256. The decoder has 10 layers, with a hidden dimension of 768, 16 attention heads, a feed- forward dimension of 4096, and the maximum sequence length of 512. Overall, the autoregressive model has 166M parameters and the Edit Bridge model has 174M parameters. The Edit Bridge model has three additional heads for A == (A Xr, >, Qi and Q respectively (Eq. (4)). Note that ReaSyn's 's models have fewer parameters than SynFormer (Gao et al., 2025) of 230M parameters, because they do not require heads for fingerprint diffusion and reaction classification.
D DETAILS ON TRAINING
In our paper, following Gao et al. (2025), we used the set of 2,222 purchasable building blocks in the Enamine's U.S. stock catalog retrieved in October 2023 (Enamine, 2023), and the set of 115 reactions that include common uni-, bi- and tri-molecular reactions curated by Gao et al. (2025)). Given the sets of building blocks and reactions B and R. the dataset of p can be generated on-the-fly during training by iteratively executing compatible reactions to random building blocks. A stack is maintained for each pathway to keep track of intermediate products during data generation, and the corresponding token blocks of building blocks and reactions are concatenated to construct sequence as described in Section 4.1.
ReaSyn's 's autoregressive model was trained using the dataset of (xx, p) pairs with the next token prediction loss (Eq. (2)). The AdamW optimizer (Loshchilov & Hutter, 2019) with learning rate 3e 4 for 500k steps was used. A batch size per GPU was set to 64, and 8 NVIDIA A100 GPUs were used. The training took about 5 days.
We implement Edit Bridge based on the codebase ReaSyn's Edit Bridge model was trained using the dataset of (æ, 2o, 21) triplets. The dataset was generated offline using our proposed Edit Bridge coupling. Specifically, given a (x, P1) pair where p1 corresponds to the true pathway of molecule æ. we first generate Po with the autoregressive model pretrained with the training procedure described above. In this paper, we used Pau without the bidirectional iterative cycle as Po- Then the aligned (zu, 21) pair is obtained via the alignment process (explained in Section B). Generating 10.5M data points with 120 NVIDIA A100 GPUs took about 3 days. Using the dataset, the Edit Bridge model was trained with the Bregman loss objective (Eq. (5)). The AdamW optimizer (Loshchilov & Hutter, 2019) with a learning rate of 3e - 4 for 500k steps was used, A batch size per GPU was set to 128, and 8 NVIDIA A100 GPUs were used.
E DETAILS ON INFERENCE
The proposed bidirectional iterative cycle explained in Section 4.2 and Algorithm 1 can be viewed as a variant of Gibbs sampling (Geman & Geman, 1984). To sample a synthetic pathway p from repeats
the distribution p(p) p(p',... ,..., p²), ReaSyn repeats the process of uniformly drawing the block index b ~ {1...., ......, B- 1} and (1) sampling p>* from the distribution p(p>* s' or (2) sampling
S from the distribution p(pSb (p="'>>>>>)) ">>). This is equivalent to performing Gibbs sampling with the kernels p(p>*1p "n"""> and p(p Sb Ip >)), allowing ReaSyn to extensively search the neighborhood of the synthetic tree space.
Inference of ReaSyn was implemented similarly to the official codebase of Gao et al. (2025). Specifically, the Transformer decoder of ReaSyn autoregressively generates pathway block-by- block, and a stack is maintained for each pathway p being generated. If the generated block pd corresponds to a molecule (x'), a building block is retrieved from the building block set B by conducting the neirest- search, and then pushed to the stack. Here, the neight
hattas/s/ggtabbb.ccm///eeeeeaaas forner (Apache-2.0 license) http:///ggghhhb..oooeeeeeeeeeeee 'https: thub. http://ghhtt...coo///ee/oeg///// (Apache-2.0 0 license)
/edii ows demo
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search uses the following similarity function:
1
sim x. x²) dist(z, x') + 0.1"
where dist(æ; x²) ) is the 1-norm distance between the Morgan fingerprints (Morgan, 1965) (radius 2, length 2048) of æ and x'' if æ' is valid. If x' is an invalid SMILES, dist(z, æ²) is calculated as the character-wise edit distance between the SMILES strings of æ and x'. If the generated block p" is reaction, the required number of reactants are popped from the top of the stack, then the reaction is executed using the RDKit (Landrum et al., 2016) library. Unlike in Gao al. (2025), if the predicted reation is incompatible with the reactants in the current stack, ReaSyn chooses the next reaction with the next highest prediction logit. The resulting intermediate product pushed back to the stack.
To extensively explore the synthesizable chemical space with multiple synthesizable analog sug- gestions, beam search is employed. Following previous works (Luo et al., 2024; Gao al., 2025), ReaSyn employs beam search which tracks the top-scoring stacks being generated and expands them block-by-block. Molecular blocks are scored by the aforementioned similarity used in the building block retrieval step (Eq. (6)). Reaction blocks are scored by the classification probability predicted by the model, and a stack's score is defined as the sum of the scores of its blocks.
All experiments were conducted using NVIDIA A100 GPUs.
E. SYNTHESIZABLE MOLECULE RECONSTRUCTION
Similarity in Table 1 and similarity (Morgan) in Table 2 measured as the Tanimoto similarity on the Morgan fingerprint (Morgan, 1965) with radius 2 and length 4096. We used Therapeutics Data Commons (TDC) library (Huang al., 2021) to calculate diversity. In Figure 4, for BU, TD, BU+TD, and BU+TD+EB, the test-time compute was scaled using different values of number of the bidirectional iterative cycles. The search width, exhaustiveness, and number of Edit Bridge samples in each cycle were set to 8, 4, and 100, respectively. The best values of BU+TD+EB were also reported in Table 1, which used 12, 24, and 16 cycles for Enamine, ChEMBL, and ZINC250K, respectively. We used the default search width of 64 and exhaustiveness of 24 for ChemProjector (Luo et al., 2024) and SynFormer (Gao et al., 2025), and used a search width of 12 for SynNet (Gao et al., 2021).
E.2 SYNTHESIZABLE GOAL-DIRECTED MOLECULAR OPTIMIZATION
In Table 3, a population size of 100 and an offspring size of 100 were used, The rate of the mutation operation of Graph GA was set 0.1. Following Gao et al. (2022) and Sun et al. (2025), the maximum number of oracle calls was set to 10k the optimization performance was evaluated with the area under the curve (AUC) of the top-10 average score versus oracle calls. We performed the grid search (search width) E {1, 2} for each oracle function. For drd2, gsk3b, perindopril mpo, sitagliptin mmp, and zaleplon mpo, the search width was set 1; for all others, it was set to 2. The exhaustiveness, the number of bidirectional cycle, and the number of Edit Bridge samples for cycle was set to 4, 1, and for all oracle functions.
In Table 4, population size of 400 and an offspring size of 100 were used. The rate of the mutation operation of Graph GA was set to O.I. The search width, exhaustiveness, the number of bidirectional cycle, and the number of Edit Bridge samples for cycle was set to 2, 4, 1, and 4, respectively. The objective function to optimize is set to sEH score SA QED, where sEH score is the normalized binding affinity of the sEH protein target predicted by a proxy model (Bengio et al., 2021), and SA and QED are normalized SA score (Ertl & Schuffenhauer, 2009) and QED (Bickerton et al., 2012) defined as:
10 SA-SA
SA = 9 E [0, 1], QED = 2 clwp 0.5) E [0, 1].
We used Therapeutics Data Commons (TDC) library (Huang et al., 2021) to calculate SA and QED. Note that in Table 4, the baselines used a total of 300k oracle calls and the average values of Ik reported. In contrast, Graph GA- ReaRennyn used only 5k oracle calls and
generated molecules the average values of 1k generated molecules generated among the 5k molecules were reported, showing very high sampling efficiency compared to the baselines.
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Table 6: Reconstruction rate (%) results synthesizable molecule reconstruction with different
train/inference schemes.
Dataset ChEMBL
Train
Inference
Enamine
ZINC250K
BU TD
BU
75.3
22.5
51.3
TD
85.2
25.6
41.4
BU+TD
BU
78.3
22.4
38.8
BU+TD
82.5
25.4
44.0
Table 7: Synthesizable molecule reconstruction results of ReaSyn that uses two separate autoregressive models for BU and TD and ReaSyn that single autoregressive model that does both BU and TD sampling.
Dataset
Method
Reconstruction rate (%)
Similarity
Div. (Pathway)
Div. (BB)
Enamine
ReaSyn-separate BU+TD
95.2
0.987
0.118
0.757
ReaSyn
95.0
0.987
0.118
0.753
ChEMBI.
ReaSyn-separate BU+TD)
31.4
0.750
0.050
0.321
ReaSyn
31.7
0.751
0.050
0.321
ZINC250K
ReaSyn-separate BU+TD)
88.1
0.960
0.074
0.681
ReaSyn
87.9
0.958
0.071
0.658
Table 8: Comparison of different couplings of Edit Flow. The values are the average of 10,000 random
training data.
Rate of align operations (%)
Coupling
Aligned rate (%) 1
of edit oss. +
Insertion
Deletion
Substitution
Empty (Havasi al., 2025)
0.00
94.55
100.00
0.00
0.00
Uniform (Havasi ct al., 2025)
2.59
142.87
14.75
36.90
48.35
Edit Bridge (ours)
70.56
30.01
26.75
25.24
48.01
E.3 SYNTHESIZABLE HIT EXPANSION
In the synthesizable hit expansion experiment, The search width, exhaustiveness, the number of bidirectional cycle, and the number of Edit Bridge samples for cycle was set to 12, 128, 12, and 100, respectively. Up 100 synthesizable analogs were collected for each input molecule.
F ADDITIONAL EXPERIMENTAL RESULTS
F.1 COMPARISON OF UNIDIRECTIONAL AND BIDIRECTIONAL TRAINING
We compared the standard unidirectional and our proposed bidirectional training/inference scheme of ReaSyn's 's autoregressive model in Table 6. The top two rows (BU train, TD train) are the models that use a single fixed direction during training and inference. The bottom two rows (BU+TD train) correspond the model that uses the bidirectional training/inference scheme explained in Section 4.2. Note that the BU train and TD train are separate models, while BU+TD train is single model that can do both BU and TD inference. As shown in the table, the bidirectional model shows no significant difference in performance compared to the unidirectional models despite using only single checkpoint.
This result can be reconfirmed in Table 7. ReaSyn-separate BU+TD indicates the method that two autoregressive models (BU train and TD train in Table 6) to implement the bidirectional iterative cycle, and ReaSyn indicates the method that only in Table 6). As shown in the table, the proposed bidirectional learnin//ifferenc scheme exhibits significant performance difference compared the unidirectional method, with much higher memory efficiency.
single autoregressive model (BU+TD train
F.2 COMPARISON OF DIFFERENT COUPLINGS IN EDIT FL.WW
We provide a comparison different couplings in Edit Flow in Table 8. Empty coupling and uniform coupling the proposed in the original paper (Havasi et al., 2025). Since these independent
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Table 9: Reconstruction rate (%) results in synthesizable molecule reconstruction AiZynthFinder (Gen- heden et al., 2020) and ReaSyn. The results are the means and the standard deviations of runs. The best results are highlighted in bold.
Dataset
Method
Enamine
ChEMBL
ZINC250K
AiZynthFinder (Genheden el al., 2020)
34.0 + 0.3
55.1 = 0.1
11.4 + 0.1
AiZynthFinder (our BBs)
79.2 + 0.4
44.3 133:02 0.2
20.5 + 0.2
ReaSyn (ours)
95.0 0.0
31.7 = 0.3
87.9 + 0.2
couplings, P1 does not contain information about Po. resulting in low aligned rates and requiring many edit operations. On the contrary, the proposed Edit Bridge coupling starts from Po which is already partially aligned with P1 (aligned rate fo 70.56%), therefore requires significantly fewer edit operations (30.01).
F.3 COMPARISON WITH RETROSYNTHESS PLANNING METIIO
Assuming all input molecules are synthesizable, the synthesizable analog generation problem can be applied to the problem of retrosynthesis planning. Since the pathways that successfully reconstruct a given input molecule in synthesizable analog generation correspond to the solved pathways in retrosynthesis planning, we compare the reconstruction rates of ReaSyn with those of state-of- the-art retrosynthesis planning method in Table 9. AiZynthFinder (Genheden et al., 2020) is an MCTS-based method that recursively breaks down given product molecule into reactant molecules to find pathways to building blocks. Note that it uses 42,554 reaction templates extracted from the USPTO reaction set (Lowe, 2017) and 17,422,831 building blocks from the ZINC stock (Irwin et al., 2012), so its solution space is much larger than that of ReaSyn, which is defined by 115 reactions and 211,220 building blocks in this paper. We also include AiZynthFinder results which uses the same building blocks ReaSyn. We used the official codebase to run AiZynthFinder.
As shown in the table, ReaSyn shows higher reconstruction rate on the Enamine and ZINC250K test sets and lower reconstruction rate the ChEMBL test set. We suspect that this is because the synthesizable space of AiZynthFinder is more in line and compatible with the ChEMBL test set than that of ReaSyn. Nevertheless, ReaSyn outperforms AiZynthFinder by large margin in the other two test sets, despite its much smaller design space. Notably, ReaSyn outperforms AiZynthFinder by particularly large margin on the ZINC250K test set, which requires out-of- distribution generalization on unseen building blocks. Retrosynthesis planning methods adopt a TD approach which sequentially infers simpler molecules to arrive building blocks, making them unsuitable for inferring pathways that consider out-of-distribution building blocks. Moreover, we emphasize that the synthesizable analog generation approach much more versatile. Only reconstruction rate can be measured using retrosynthesis planning methods, and they cannot be applied to other tasks, such as suggesting synthesizable analogs given unsynthesizable molecules, goal-directed optimization of the end products, or synthesizable hit expansion.
F.4 GENERATED EXAMPLES
We provide examples of reconstructed Enamine molecules in synthesizable molecule reconstruction in Figure 8 and Figure 9. generating these examples, the search width, exhaustiveness, number of bidirectional cycles, and number of Edit Bridge samples in each cycle were set to 2, 4, 1, and 100, respectively, and ZINC250k building blocks were included. We also provide additional examples of hit molecules and generated synthesizable analogs in JNK3 hit expansion (Section 5.4) in Figure 10.
/tt zynthffindr (MIT license)
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Target molecule
Similarity: 0.189
cBU)--C C1- 712
Fclccce(Chcc
RXN:103
Similarity: 0.530
120/
[NI-NN HICCC
RXN-103
BON4
Similarity: 1.000
RXNN44
Figure 8: Examples of ReaSyn's generation cycle. All sequences are represented in bottom- up order.
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molecule
Similarity: 0.776
2
C4
IXN-10R
Similarrity: 0.779
cclec:clleDDCC002cc
RXN-22
CF
Similarity: 1.000
C2
RXN-22
Figure 9: Examples of ReaSyn's generation cycle (continued). All sequences represented in bottom-up order.
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Hit malecule
Sy st hisiabbl analogs
a.Po,
Po.Po
0-000-
d'o
JNK3 score - 0.68
JNK3 soore 0.74
JNK3 score 0.73
JNK3 score 0.73
JNK3 score 0.73
JNK3 score 0.72
0.674
sim D.850
sim 0.633
sim 0.773
JNK3 score = 0.67
JNK3 spore 0.67
JNK3 score 0.64
JNK3 score 0.64
JNK3 score 0.56
JNK3 score 0.56
sim = 0.971
sim 0.872
sim 0.850
sim 0.810
sim 0.810
2,00
INK3 score 0.56
JNK3 soore 0.63
INK3 score 0.57
JNK3 score 0.56
JNK3 soore 0.50
JNK3 score 0.48
sim - 0.923
sim 0.787
sim 0.686
sim 0.667
sim 0.567
JNK3 score - 0.62
JNK3 soore 0.57
INK3 score 0.51
JNK3 score 0.51
JNK3 soore 0.49
JNK3 score 0.48
sim 0.673
0
JNK3 score 0.55
JNK3 soore 0.64
JNK3 score 0.63
JNK3 score 0.63
JNK3 score 0.61
JNK3 score 0.60
sim 0,800
sim 0.786
sim 0.786
sim 0.833
sim 0.959
JNK3 score = 0.51
JNK3 spore 0.61
JNK3 score 0.55
JNK3 score 0.53
JNK3 score 0.53
JNK3 score 0.52
sim = 0.745
sim 0,604
sim 0.756
sim 0.756
sim 0.667
Arma
FURMOO
Roma
ooo Pomo
g aomoo
JNK3
JNK3
JNK3 sim = 0.875
JNK3
JNK3 sim 0.961
sim = 0.875
sim 0.875
sim 0.961
Q40
Op*
ouhb
Spoupp
JNK3 score 0.50
JNK3 soore 0.79
INK3 score 0.79
JNK3 score 0.79
JNK3 soore 0.78
JNK3 score 0.69
0.704
sim 0.691
sim 0.691
sim 0.704
sim 0.576
PA8
B
JNK3 score 0.49
JNK3 soore = 0.92
INK3 score 0.88
JNK3 score 0.63
JNK3 soore 0.59
JNK3 score 0.57
sim 0.695
sim 0.727
sim 0.714
sim 0.683
sim 0.656
She
JNK3 score 0.49
JNK3 soore 0.57
INK3 score 0.57
JNK3 score 0.57
INK3 soore sim 0.739
JNK3 score 0.56 sim 0.644
0.761
sim 0.604
sim 0.604
Figure 10: Examples of hit molecules and generated synthesizable analogs by ReaSyn in JNK3 expansion. JNK3 inhibition score measured by the JNK3 proxy and similarity to the input hit are provided at the bottom of each generated analog.
23


Reviews:
[{"review_id": "q8dsT0EntV", "reviewer": "Reviewer_gV5V", "summary": "The paper uses an autoregressive models to construct synthesis trees for molecules on bottom up or top down order. this is then used to perform molecular design effectively.", "questions": "- what is the validity rate for the generated SMILES building blocks?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- strong empirical results, extensive validation\n- interesting use of edit bridge model\n- great use of BU and TD combined model", "weaknesses": "1) The edit bridge model should be explained in more detail, both in theory and also how practically the implementation is done.\n\n2) Typo: In Section 4 I think the building block space should be larger than the reaction space?\n\n3) The contribution of the paper is methodically good, but unfortunately, the paper currently ignores most of the pioneering work in the area. This needs to be rectified before a higher score can be assigned.\n\nMissing citations to prior work:\n\nAutoregressive Chemical Language Models that process and generate SMILES were introduced in 2017 in \nhttps://arxiv.org/abs/1701.01329 \nInterestingly, this paper also features a discussion around reaction driven design - the field has seems to have gone full circle, but the problems have been known already ~10 years ago :)\n\nSynthesizability Projection was introduced by Bradshaw https://arxiv.org/pdf/2012.11522 see appendix D3\n\nModern AI-driven Synthesis Planning was introduced in https://www.nature.com/articles/nature25978 (2018) not in 2019.\n\nMost TDC oracles used in the paper were not introduced by TDC but taken from Guacamol https://doi.org/10.1021/acs.jcim.8b00839 originally developed at BenevolentAI - please cite the original work.\n\nfor Morgan Fingerprints, I'd suggest to cite https://pubs.acs.org/doi/10.1021/ci100050t as well", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rNhOZ4gfkm", "reviewer": "Reviewer_xjoZ", "summary": "This paper proposes ReaSyn, an iterative, bidirectional pathway–generation framework that combines (i) bottom-up decoding, (ii) top-down decoding, and (iii) a holistic editing step (“Edit Bridge”) to project molecules into a synthesizable space and improve coverage on reconstruction, goal-directed optimization, and hit expansion. The technical idea is sound and the empirical results are strong, but parts of the presentation make the method look more general than it actually is. If the authors clarify the core contribution, the experimental setup, and the fairness of the comparisons, I would support acceptance.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "- A novel way of combining top-down, bottom-up, and holistic edit steps in one framework (effectively three networks working together).\n- Effective test-time scaling: adding TD and the Edit Bridge gives non-trivial gains.\n- Strong results on the harder, expanded-stock setting.", "weaknesses": "- Misleading Figure 1 and Table 1. The chemical space is **not** ZINC; ZINC is used to augment the building-block set. As written, the figures can easily be read as “reconstructing ZINC,” which is inaccurate. Please rename/reword.\n- Time cost in Figure 4. The iterative pipeline appears pretty costly, especially for non-Enamine compounds. Please report the inference time for SynFormer in the figure/table.\n- Fairness of comparison. ReaSyn’s gains partly come from using three networks and scaling at inference. Comparing this directly to single-pass baselines is somewhat unfair. The paper should clearly state the main contribution and separate the gains from architecture vs. extra inference compute.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PAZBHrC8U6", "reviewer": "Reviewer_RDTc", "summary": "The paper proposes ReaSyn, a framework for synthesizable projection that turns an arbitrary target molecule into one or more synthesizable analogs together with explicit synthetic pathways. The key ideas are:\n\n1. a sequential pathway representation that encodes both molecules (via SMILES blocks) and reaction types in a single token vocabulary, enabling bottom-up (BU) and top-down (TD) traversals of a synthesis tree using the same autoregressive Transformer;\n\n2. a bidirectional iterative cycle that alternates BU sampling (from building blocks up) with TD subtree re-generation (from the product down), propagating edits throughout the pathway; and\n\n3. Edit Bridge, a discrete-flow editor over full pathway sequences (insertion/deletion/substitution) trained with offline alignments to holistically refine the pathway beyond local autoregressive edits.\n\nOn synthesizable molecule reconstruction across Enamine, ChEMBL, and a harder ZINC250k-augmented setting, ReaSyn reports markedly higher reconstruction rates, similarities, and diversities than prior synthesizable-space baselines. When plugged into Graph-GA as a projection/mutation step, ReaSyn maintains strong goal-directed optimization performance while ensuring synthesizability, and it outperforms prior synthesis-aware methods on TDC oracles, an sEH proxy (with improved SA/QED and AiZynth success), and JNK3 hit expansion (higher analog, improve, and success rates).", "questions": "1. Catalog & rule portability. How does performance shift if (a) the building-block catalog changes (e.g., a newer Enamine snapshot or a different vendor), and (b) the reaction set is expanded/altered? Any zero-shot results with unseen rules?\n\n2. Edit Bridge hygiene. How are $(p_0,p_1)$ pairs generated to prevent leakage from evaluation targets? Do you withhold all evaluation molecules/routes from the edit-alignment dataset?\n\n3. Route executability. Beyond AiZynth success, can you provide route-level checks (e.g., selectivity flags, incompatible functional groups, protecting-group needs) or human/chemist audits on a subset of proposed pathways?\n\n4. Policy control. Did you try non-uniform TD block selection (e.g., uncertainty or mismatch heuristics) and adaptive stopping for the BU↔TD loop? Any gains in sample efficiency?\n\n5. Scaffolds vs. properties. For optimization/hit expansion, how does performance trade off between scaffold retention and property improvement as you vary the similarity threshold?\n\n6. Ablations at fixed cost. If you fix total decoding steps, how do (i) more BU↔TD iterations vs. (ii) more Edit Bridge steps vs. (iii) larger beam affect outcomes?\n\n7. Failure taxonomy. What are the most common failure modes (e.g., rule conflicts, unreachable leaves, unstable intermediates), and how often does Edit Bridge repair them vs. induce new ones?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Unification of BU and TD in one policy. Clever direction control (first-token bias) plus balanced token-type loss yields a single model capable of both traversals; the iterative BU↔TD cycle is a natural way to propagate local edits through the pathway.\n\nSimpler, more expressive representation. Replacing hierarchical, fingerprint-based encodings with SMILES blocks and reaction tokens reduces architectural complexity and avoids fingerprint information loss/sparsity.\n\nHolistic refinement with Edit Bridge. A discrete-flow editor that operates at the full pathway level complements autoregressive edits and demonstrably improves reconstruction/diversity in ablations.\n\nStrong empirical results across tasks. Large gains on reconstruction (including OOD ZINC250k building-block expansion), competitive or better optimization on TDC oracles while keeping synthesizability, improved SA/QED/AiZynth on sEH, and substantially better hit-expansion metrics for JNK3.\n\nThoughtful ablations. Clear attribution for (i) bidirectional iteration vs. single-direction, (ii) Edit Bridge vs. none, and (iii) representation differences vs. a close BU baseline with a near-identical Transformer.\n\nPractical orientation. Uses a fixed reaction rule set and purchasable building-block catalog, aligning evaluation with real synthesis constraints; integrates cleanly as a projection module in standard optimization loops.", "weaknesses": "Compute transparency. The paper mentions a large offline alignment corpus for Edit Bridge and multi-stage decoding/beam search, but lacks wall-clock, FLOPs/tokens, and beam/budget controls, making fairness vs. baselines (and scaling laws) hard to judge.\n\nSearch-policy details. The TD subtree choice is uniform random over blocks; more informed selection might improve efficiency/quality. Ablations over the number of BU↔TD iterations vs. Edit Bridge steps at matched compute are limited.\n\nData hygiene & leakage. Reconstruction uses molecules drawn from the same vendor catalogs/rule families that likely seed training. Clear train/val/test separation for pathway patterns (and for the offline $(p_0,p_1)$ edit pairs) is under-specified.\n\nMetric sensitivity. Similarity relies mainly on Morgan-Tanimoto; scaffold/pharmacophore scores are included in one benchmark, but systematic metric sensitivity and rank correlations across tasks are not fully explored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6QZ5WPCc7l", "reviewer": "Reviewer_5E2P", "summary": "- This paper addresses the problem of synthesizable molecule generation, which aims to generate molecules that are synthetically accessible.\n- The authors propose ReaSyn, an iterative generative pathway retirement framework based on reaction trees. It effectively generates both molecules and their corresponding synthetic pathway (i.e., how to synthesize the molecules).\n- The results demonstrate superior performance compared to state-of-the-art methods for synthesizable molecule generation.", "questions": "See the weakness.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well written and clearly structured.\n- It overcomes existing difficulties in tree-structured synthetic pathway generation, which are typically tackled using bottom-up or top-down approach. By integrating bottom-up decoding, top-down decoding and Edit bridge (recently propsoed), the proposed model allows to iterative generation and refinement of the generated pathways. I think this integration is a novel contribution of this paper, even though the individual components of ReaSyn are known and commonly used in pathway generation.\n- The results clearly show that ReaSyn outperforms state-of-the-art models across various evaluation metrics.", "weaknesses": "The generation cycle consists of 3 steps: bottom-up generation, top-down generation, and Edit bridge for pathway retirement, while the compared models use only a single step. It would be helpful to discuss how much slower ReaSyn is in terms of training and inference time (generation of pathway), compared to other methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)