Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Authors: Piotr Gaiński
Oussama Boussif
Andrei Rekesh
Dmytro Shevchuk
Ali Parviz
Mike Tyers
Robert A. Batey
Michał Koziarski
Venue: NeurIPS 2025 poster ()
Keywords: drug discovery
generative models
GFlowNets
synthesizability
molecular generation
Primary area: machine_learning_for_sciences

Abstract: Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose **Recursive Cost Guidance**, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an **Exploitation Penalty** that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a **Dynamic Library** mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Piotr Gainski*, Oussama Boussif Andrei Rekesh", Dmytro Shevchuk Ali Parviz
Mike Tyers34, Robert A. Batey Michal Koziarsk
Jagiellonian University, Faculty of Mathematics and Computer Science,
Mila Québec Al Institute, 3 Université de Montréal, University of Toronto," The Hospital for Sick Children Research Institute, Accelcration Consortium, Vector Institute, Equal contribution piotr. deiiski@@octtrre uj edu. pl
Abstract
Template-base molecular generation offers a promising avenue for drug design by ensuring generated compounds synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling large building block libraries, and (3) effectively utilizing small fragment sets. We propose Recursive Cost Guidance, backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways. significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an Exploitation Penalty that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a Dynamic Library mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results template-based molecular generation.
Introduction
Generative models hold significant promise for accelerating drug discovery by enabling direct sampling of molecules with desired properties, vastly expanding the accessible chemical space. However, despite increasing interest from the machine learning community, their adoption in practical screening pipelines remains limited, primarily due to the poor synthesizability of the generated compounds. Recent research has begun to address this synthesizability bottleneck.
One prominent direction involves directly optimizing synthesizability. either by including synthe- sizability scores as part of the reward function [ or by using retrosynthesis models guide coal-conditioned generation [ However, such approaches remain fundamentally unreliable: heuris- tie metrics such as SAScore often correlate poorly with true synthetic feasibility, while learned models such as RetroGNN struggle with limited training data and poor generalization.
An increasingly popular and more principled alternative is template- based synthesis, where molecules are assembled from predefined building blocks through cell-charatteizedd reactions [5] 6). This paradigm mirrors laboratory synthesis more closely and, with appropriate reaction templates, can provide an approximate guarantee of synthesizability. Recent work has embedded this formulation within Generative Flow Networks (GFlowNets) [7]. which are well suited molecular discovery due to their capacity for mode-seeking sampling and efficient exploration of combinatorial spaces [8 10
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Recursive Cost Guidance for P
b) Exploitation Penalty for Py
Dynammi Building Block Library
(6m)
Sampled bepekem
P3 PÉ
Py x (dola)
Pe (a, n(s.a)"
backward sampling
forward sampling
Figure I: a) Recursive Cost Guidance employs machine learning models et and 2B to estimate the intractable synthesis cost and decomposability of precursor molecules, guiding the backward policy Pn toward cheaper and viable intermediates. b) Forward policy is augmented with Exploitation Penalty counter overexploitation induced by Synthesis Cost Guidance component of Pn. e) Dynamic Library gathers intermediate molecules with the highest expected reward and adds them to the building block library M, enabling full-tree synthesis.
Despite this progress, key limitations remain, particularly in terms of scalability and cost awareness. An ideal synthesis-aware generative model would explore the full space of synthesizable molecules while favoring those that simpler and cheaper to make. Existing methods fall short: RGFN [8] employs small, curated fragment library optimized for wet lab feasibility, but this restricts scalability. In contrast, RxnFlow [TO] improves scalability through larger libraries and broader exploration, but ignores the cost of the synthesis, reducing its practical utility in downstream screening campaigns.
In this paper, propose SCENT (Scalable and Dost-Efffiieett de Novo Template-Basee Molecular Generation) which addresses the aforementioned limitations of reaction-based GFlowNets. Our contributions be summarized as follows (graphical summary in Figure
We introduce Recursive Cost Guidance for the backward policy, which utilizes machine learning model to approximate the recursive cost of backward transitions. From this, we derive two targeted strategies: Synthesis Cost Guidance, which reduces synthesis cost and improves diversity large building block settings; and Decomposability Guidance, which enforces the validity of backward transitions by discouraging transitions through indecomposable molecules.
We analyze the exploitative behavior induced by Synthesis Cost Guidance and propose Ex- ploitation Penalty, simple yet effective regularizer that promotes exploration by penalizing repeated state-action pairs. This improves performance across all benchmarks.
We develop Dynamic Library mechanism that augments the initial set of building blocks with high-reward intermediates discovered during optimization. This mechanism enables full-tree synthesis and increases mode coverage in both low- and high-resource scenarios.
We benchmark recent reaction-based GFlowNets across three library sizes and three design tasks, demonstrating that SCENT significantly outperforms prior methods in terms of synthesis cost, molecular diversity, and the number of high-reward compounds discovered.
Our code is publicly available at hbttpss//gtthu..cccoooouaaaassss
2 Related Works
Molecular Generation. A broad range of machine learning approaches has been proposed for
small molecule generation, varying in both molecular representations such as graphs, SMILES, or 3D coordinates-ant generative paradigms, including diffusion models 11414) flow matching
17] reinforcement learning 20), and large language models (LLMMs) 24. Among these, Generative Flow Networks (GFlowNets) 7 25-28 have emerged compelling alternative to

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reinforcement learning for iterative molecular generation. Their ability to perform diverse, mode- covering sampling makes them particularly well suited for drug discovery.
Synthesis-Aware Molecular Generation. Synthesis-aware molecular generation has gained mo- mentum strategy ensure that generated molecules are not only
but also synthetically accessible. These methods construct molecules from predefined building blocks using high-yield, cell-characterized reactions. Recent GFlowNet-ben approaches ||| 10. along with
reinforcement learning (), genetic algorithms [29], and autoregressive models 330 incorporate reaction-awee aware priors to produce more realistic candidates.
Backward Policy Design in GFlowNets. In Generative Flow Networks (GFlowNets), the most common training objective match the forward policy PF and the backward policy PB to satisfy the trajectory balance condition [I While PB is typically learned jointly with Pr. it can also be optimized separately or fixed influence the behavior of Pp. For example, Jang al. [32] train PB via maximum likelihood over observed trajectories to induce exploitative behavior in Pr, whereas Mohammadpour et al. [33 use fixed maximum-entroppy entropy PB encourage exploration.
Guided Trajectory Balance (GTB) 34 is the most relevant prior work on backward policy guidance. GTB introduces preference distribution to match it. In contrast, we define trajectory preference through a recursive cost function, yielding more interpretable and flexible formulation. We approximate this cost using an auxiliary model, which gives rise to our proposed Recursive Cost Guidance framework that steers generation toward low-cost synthesis pathways while improving consistency with the underlying Markov decision process.
trajectories to given terminal state and trains PB
SynFlowNet [9] also modifies Pp. using reinforcement learning discourage transitions through nondecomposable intermediates. While it addresses similar challenges, our method provides a more general and principled solution by unifying synthesis cost reduction and MDP-consistent trajectory shaping through backward policy design.
Library Learning. Library learning is long-standing strategy for managing large search spaces in domains such as program induction and reinforcement learning. DreamCoder (75, for example, extracts reusable subroutines from sampled programs to build library of primitives. Similarly, the Options framework [38] and macro-actions (39] learn temporally extended actions to facilitate exploration and improve credit assignment. More recently, ActionPiece [40] applied byte pair encoding (BPE) to compress frequent action subsequences in GFlow trajectories, augmenting the action space with learned macro-actions.
Our Dynamic Library builds on this foundation but shifts from action abstractions to state abstrac- tions. Rather than compressing action sequences, we identify and cache high-reward intermediate states, which act as reusable substructures for constructing nonlinear synthesis trees. While con- ceptually similar to DreamCoder's structural reuse, approach operates in the space of synthesis pathways, a novel application of library learning principles to generative models for molecular design.
Method
In this section, we present SCENT and its core components: (1) Recursive Cost Guidance, general backward policy framework that estimates recursive transition costs using a learned model: (1.1) Synthesis Cost Guidance, which biases generation toward low-cost synthesis pathways; (1.2) Decom- posability Guidance, which discourages selection of indecomposable molecules: (2) Exploitation Penalty, which counterbalances the exploitative bias introduced by Synthesis Cost Guidance (see Section
(4.4) and improves overall performance; and (3) Dynamic Building Block Library, which expands the building block set with high-reward intermediates to enable full-tree synthesis and trajectory compression. The complete pseudocode of SCENT's training procedure is provided in Appendix A.I
3.1 Preliminaries
Like other reaction-based GFlowNets 8-10, SCENT operates on a predefined set reaction templates R and building blocks M. Its forward policy Pr. adapted from RGFN (Appendix A.2).
3

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constructs molecules via sequential application of reactions. The backward policy PB. in turn, decomposes molecule by reversing these reactions. Specifically, given an intermediate molecule m. Pn samples tuple (m', MM .r) where m' is a precursor molecule, M' is a tuple of building blocks from M, and r R is reaction such that applying r to m' and M' yields m. For clarity, we denote molecules m and m' as states and s', and the pair (M', r) as an action a', so that the backward policy is defined as IBB(('', (s'(s'') | s).
The backward policy in SCENT'S is constructed using the Recursive Cost Guidance framework introduced in Section 3.2 It composed of two sub-policnn policies: the Synthesis Cost Guidance policy P (Section 3.2.1 and the Decomposability Guidance policy PB (Section 3.2.2
3.2 Recursive Cost Guidance
Recursive Cost Guidance is new and general framework for backward policy guidance that employs a machine learning model to approximate the recursive costs of backward transitions. Let cy (To:n) be a function that assigns cost a forward trajectory To.2 = (s0, ao...... ... 8n.) starting from source
state sp:
(I) where C' defines the cost of transition from 8n-1 to Sn via action Gn-1, possibly depending on the cumulative cost (r(T).--)) A natural instantiation of oF for template-basee GFlowNets is the cost of the synthesis pathway, described in Section 3.2.1 To guide the backward policy Pn toward
CF cF(TT:: (T)..) C(Cr(Ton 1), 8n- 1,
trajectories with lower forward cost cr, we define a backward cost function cg that represents the cost of the cheapest forward trajectory reaching given state
cB(s) = min cp(T), rET...
where T-,s is the set of all trajectories leading to state This can be reformulated into:
CB (5) = min C(cB(s'), s' s''',,) (s"a"))SAA'
where SA' is a set of all parent state-action tuples. Since eg is defined recursively, exact computation is generally intractable. To address this, estimate cg using a machine learning model čg. and define recursively guided backward policy PB:
Pa((s(, (o((%),.))))) a. (1) L 4= =0 8/, d4, 8),
where a controls the greediness, and 1 is the vector of logits 4. This formulation avoids expensive recursive evaluations of cB by leveraging an inexpensive surrogate čg (see Figure 2). To train êB, we maintain fixed-size dataset D containing current estimates of cB(s) denoted čω (s). After each iteration of forward policy training, we update these estimates using the encountered trajectories Tuxn by applying: (s:) = min(Cz ($:(),), cr (To:)). The model čn is then fitted to D using a dedicated cost loss Cosst- In this work, we design two specific cost models - one for Synthesis Cost Guidance and another for Decomposability Guidance. The details of the models can be found in Appendix A.3
3.2.1 Synthesis Cost Guidance
Even though template- baalld approaches, if used with a curated set of reaction templates and fragments, can guarantee high synthesizability, the cost of synthesis of different molecules can vary widely depending on the cost of reactants and the yields of reactions. RGFN [8] approaches this issue by defining highly curated set of low-cost fragments and high-yield reactions. However, this comes at the price of reducing molecular diversity and reachable chemical space. Instead, we propose defining more extensive fragment library, but guiding the generation process toward cheaper molecules.
We estimate synthesis cost using stock prices of building blocks and reaction yields:
(5) where x represents either the accumulated cost of synthesis pathway (in Equation O6) or the minimal cost to synthesize a molecule (in Equation 77).. The term c(a') denotes the total cost of all reactants involved in the action a' (a' includes the building blocks M' and the reaction template
(x,, s''''''' s) = (a + c(a') (((')))ss',, d',s)_
r). The reaction yield y(s', a', 8) is the expected success rate of the reaction leading from reactants
{s') U A' to the product s via the template Details on how yields and building block costs are
4

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TOO
cheap model 4
"CA
dn(s) cx
molecule from dynamic limry
recursive
Recursive Cost Guidance framework uses cheap model êø to approximate the in- tractable recursive cost cg backward transi- tions.
Figure 2:
Figure 3: Dynamic building block library is augmented using high-reward molecules (de- picted in pink) that occur during sampling.
estimated are provided in Appendix While other factors, such as reaction time or solvent cost, may influence synthesis cost, they are either highly lab-specific or typically negligible. Consequently, our formulation of S offers a robust and broadly applicable first-order approximation.
We integrate C into the forward and backward cost formulations (Equation (Ton)) C (c (Tomm-)) Sn-,,) 1n-11 (s) min C (c sdd, (s'aa))SA
and 3 follows:
As in Section 3.2 we use machine learning model as approximate the intractable of
( SA'
( (1), -a5 (c,, (s)) s d,,
(8)
Further architectural and training details for c provided in Appendix A.3 Our experiments show that the use of PS significantly reduces the cost of the proposed synthesis routes while dramatically increasing the number of distinct modes discovered (Section
3.2.2 Decomposability Guidance
A core challenge in template-bas based GFlowNets is ensuring that the parent state s'. sampled by the backward policy Pı ((s', a') s), recursively decomposable into building blocks. RGFN addresses this by explicitly verifying decomposability. but this incurs significant computational cost [ In contrast, Cretu al. [9] propose two implicit strategies: (1) a reinforcement learning approach that assigns positive rewards to trajectories reaching the source state 80 and negative rewards to those terminating non-decomposable intermediates; and (2) a pessimistic backward policy trained via maximum likelihood over aarward-sampled trajectories to implicitly favor valid paths.
In this work, we take different approach by leveraging our Recursive Cost Guidance framework to address decomposability. Specifically, we define backward cost function that penalizes invalid trajectories that do not begin at a source state:
(
min (s'),, (S))))
(9)
with the base case cB(ss) 0 is source state and S.A' 0. and (s) otherwise. We then define the backward policy PR. leveraging
oft model that approximates the intractable cost c..
pd (s$ af) 8) = (sA'l o (I), 4 =p=(((((s),
(10)
Further architectural and training details for 2p are provided in Appendix A.3 Decomposability Guidance is a critical component of SCENT, substantially influencing overall performance (see Appendix [.I). Unless otherwise noted, all experiments in this paper assume that SCENT Decomposability Guidance. When Synthesis Cost Guidance is also enabled, SCENT combines the two by taking the product of their corresponding backward policies:
PE((s', a') P ((s', . d') s) ( s).
(11)
5

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3.3
Exploitation Penalty
As shown in Section 4.4 incorporating Synthesis Cost Guidance into SCENT induces more exploita- tive behavior. To counteract this tendency, we introduce a simple yet effective strategy that penalizes repeated action choices given state. Specifically, we augment the forward policy Pp with an exploration-aware component Pe:
(n(s,a) + +c)
PF(a Pg(a [s), Pg(a 8)
(12)
(n (') +c)-7
Here, n(ss, a) denotes the number of times action has been selected state 8, > is smoothing term, and > 0 controls the strength of the penalty.
Unlike pscudo-count methods that rely approximate density models 41H43), our method uses exact counts, avoiding estimation artifacts and simplifying implementation. Although this count- based approach requires explicit storage of visited state-action pairs-which could be problematic in large state spaces- we show in Section 4.4 that applying this penalty only during the initial training phase (e.g., the first 1,000 iterations) is sufficient to yield consistent exploration benefits. This keeps the memory footprint bounded and independent of the total number of training iterations.
3.4 Dynamic Building Block Library
Current template-basee based GelowNets generate only left-leaning synthesis trees by drawing reactants from a fixed set of building blocks (Figure B). We extend this setup with Dynamic Building Block Library that augments the block set with high-reward intermediates discovered during training. These intermediates act as reusable subtrees, enabling the construction of richer, tree-structured synthesis pathways. This not only expands the space of reachable molecules (more down in the section) but also allows trajectory compression (rewriting paths into compact trees), enhancing credit assignment and training efficiency.
Every T training iterations, intermediate molecules encountered during the training are ranked by their expected utility Us)::
1
Uss) = ITa
Σ R(sr), TET.
(13)
where T, is the set of trajectories containing molecule and R(s,) is the reward assigned by GFlowNet to the final state of the trajectory т. The top-L intermediates, compatible with known reaction patterns, are added to the library. This procedure repeated up to Nuaa times. This approach significantly improves the performance of SCENT in all tested configurations (see Section 4
To assess the extent to which Dynamic Library expands the space of reachable molecules, we randomly generated 10,000 balanced binary synthesis trees (emulating Dynamic Library) and attempted to decompose each resulting molecule into synthesis paths accessible SCENT without Dynamic Library. We found that 43% of the synthesis trees could not be decomposed, indicating that Dynamic Library increases the number of reachable molecules by up to 75%.
4 Experiments
In Section 4.2 we compare SCENT against other template-basse GFlowNets and show the con-
tribution of individual SCENT's components to overall performance. Section 4.3 analyzes the cost reduction mechanism behind the Synthesis Cost Guidance, Section 4.4 highlights its inherent exploitative behavior, motivating the Exploitation Penalty, and Section 4.5 demonstrates the scaling properties of the Synthesis Cost Guidance. Finally, Section 4.6 that SCENT displays improved synthesizabilityy compared to other approaches.
4.1 Setup
Previously reported results for template-based GFlowNets are not directly comparable due to differ- ences template and building block sets. To ensure fairness, we introduce a unified benchmark with standardized templates and building blocks. We evaluated models in three settings: (1) SMALL:

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curated fragments (see Appendix ED for rapid synthesis or high- high-throughput screening, (2) MEDIUM: SynFlowNet's templates with 64k Enamine building blocks, and (3) LARGE: like MEDIUM, but with 128k building blocks. These settings span practical use cases: SMALL focused on rapid synthesis and MEDIUM/LARGH supporting broader exploration requiring external procurement.
The models are tested in the sEH proxy task 77. along with the GSK33 and JNK3 tasks 44 (see
Appendix for results on these tasks). We report metries for molecules discovered during training, including:
(1) the number of high-reward molecules with pairwise ECFP6 Tanimoto similarity below
0.5, and (2) the number of unique Bemis-Mureko scaffolds among high-reward molecules (where high-reward is defined by threshold). Additionally, we sample 1,000 synthesis paths and report: (1) the average reward of resulting molecules, and (2) the average synthesis cost of high-reward paths. Full training details and hyperparameters are in Appendix B
4.2 Evaluation of Template-Basee Baaed Generative Models
In this section, evaluate SCENT against recent caaplate-based GFlowNet models: RGFN (8)
SynFlowNet [9], and RxnFlow [IO]. These models explicitly generate reaction pathways. allowing for direct comparison of the synthesis costs of the proposed molecules. Table reports the results
of the evaluation on the sEH proxy task with a reward threshold of 8.0. Additional results for the
GSK33 and JNK3 tasks, as well for the sEH task with lower threshold of 7.2, are in Appendix F
Introducing Synthesis Cost Guidance (C) into SCENT consistently reduces the synthesis cost across all settings while also improving the average reward. Importantly, it significantly increases the number of high-rewatt reward scaffolds in the MEDIUM and LARGE settings across all tasks. In the sEH task at the 8.0 threshold, Cost Guidance also substantially increases the number of high-reward modes; however, a slight decrease is observed at the 7.2 threshold. This indicates a shift toward more exploitative behavior, with the model focusing on high-reward regions. We analyze this trend in detail in Section 4.4 where we also motivate the introduction of the Exploitation Penalty.
Adding the Exploitation Penalty (P) to SCENT with Synthesis Cost Guidance (C+P) leads to con- sistent performance improvements across all settings and tasks, without sacrificing cost reduction. Notably, P recovers and surpasses the number of discovered modes at the 7.2 threshold, effectively mitigating the over-exploitation introduced by Synthesis Cost Guidance.
Incorporating the Dynamic Library (D) into SCENT with Synthesis Cost Guidance (C+D) yields further gains across all settings. These improvements are attributed to better credit assignment in compressed trajectories. The combination of P and D proves especially effective in the SMALL setting, suggesting complementary roles in improving exploration under constrained conditions.
Ultimately, SCENT with C+D+P in SMALL and C+P in MEDIUM and LARGE achieves the best overall performance in terms of diversity, quality, and cost of synthesis. These results highlight Cost Guidance's 's dual role in reducing the cost of high-reward molecules and enabling effective scaling to larger building block libraries, explored in Section 4.5
The qualitative analysis of how Synthesis Cost Guidance and Dynamic Library changes the generated trajectories can be found in Appendix G
4.3 Synthesis Cost Reduction Mechanism
We analyze how Cost Guidance reduces the average cost of the reaction path generated by PF. As shown in Figure 4a, Synthesis Cost Guidance shortens trajectory lengths, thereby decreasing overall path costs. Furthermore, Figure 4 and -d demonstrate that Cost Guidance selects cheaper fragments and significantly limits the use of more expensive ones. Finally, Figure Je illustrates that the Synthesis Cost Guidance relies on subset of fragments throughout training, which correlates with the frequent use of cheaper molecules. The corresponding analysis for the SMALL setting provided in Appendix H. We perform an ablation study on different cost reduction methods in Appendix J.2.2 showing that Synthesis Cost Guidance consistently identifies more modes and higher-reward scaffolds, at lower costs.
4.4 Synthesis Cost Guidance Exploitativeness Analysis
7

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Table 1: Template-base basee GelowNets the sEH proxy SMALL, MEDIUM, and LARGE settings. SCENT (C) significantly reduces molecule cost and boosts scaffold and mode discovery in MEDIUM and LARGE. Dynamic Library (C+D) improves performance in all settings, while adding Exploitation Penalty (+P) yields the best overall results, making SCENT the strongest method.
online discovery
inference sampling
model
modes > 8.0
scaff. >> 8.0
reward 1
cost >> 8.0 4
RGFN
538 #:1
5862 1354
7.34
#:006
37.7 #20
SynFlowNet
10,0 =088
27.0 #8.6
6,89
#0.29
RxnFlow
11.3 =500
29.7 +12.7
4.18 =0.72
SCENT (w/o C)
510 #26
5413 #334
7.38 #0.02
37.1 11.0
SMALL
SCENT (C)
478 111
5150 187
7.43
23.7
SCENT (D)
557 16
6496 1:34
7.42 10.02
32.8 119
SCENT (P)
644 16
7840 1169
7.33
36.0 114
SCENT (C+D)
596 119
7148 +:97
7.56 #0.05
19.7 2200
SCENT (C+P)
595 +:0
6839 1360
7.42
20.7
SCENT (C+D+P)
715 155
8768 1:63
7.66 10.03
20.7 13.0
RGFN
4755 1:541
10638 1:918
7.08 10.08
1268 171
SynFlowNet
288 1:28
299 1:32
6.38 10.03
1952 1:548
RxnFlow
26.3 #3,9
27.3 #2.5
6.3 =0.02
MEDIUM
SCENT (w/o C)
9310 +863
11478 +:23
7.31 #0.09
1463 =62
SCENT (C)
17705 +4224
52340 24303
7.74 =0.04
1163 147
SCENT (D)
8629 1889
10605 1:34
7.21
16.166
1522 1:1
SCENT (P)
13270 1647
17093 1:570
7.32 20.04
1462 1:6
SCENT (C+D)
17241 166
52607
1:352
7.71
20.03
1141 1:6
SCENT (C+P)
37714 1:430
90068
190000
7.81
20.04
1230 1121
SCENT (C+D+P)
32761 +148
86081
1:1771
7.75
10.08
1117 1:3
RGFN
5242 1123
6215 199
7.09 10.11
1725 1153
SynFlowNet
278 12222
385 1.101
6.39 10.19
RxnFlow
24.5 12.
25.0 1200
6.14 10.00
SCENT (w/o C)
7171 12911
8767 1:429
7.13 10.12
1678 1:6
LARGE
SCENT (C)
12375 #264
36930 ::55
7.52 20.09
1267 159
SCENT (D)
6384 1478
7956 1600
7.2 #0.11
1656 140
SCENT (P)
4594 1414
8475 #:77
7.22 20.05
1779 185
SCENT (C+D)
17138 +1341
46443
22080
7.68 20.11
1256 107
SCENT (C+P)
26367 43193
46202 10242
7.76 20.03
1291 166
SCENT (C+D+P)
24434 44223
47816
+:538
7.78
+0.1
1399 166
The results Section 4.2
together with the cost reduction
Scaffold revisits with reward
mechanism detailed in Section 4.3 suggest that Synthe- sis Cost Guidance enhances the exploitative behavior of
SCENT (win CI
SCENT (C)
Reviatts
SCEMT (C+
0.30
Pr. To validate this, we plot how often Pr revisits pre- viously discovered high-reward scaffolds during training Figure SCENT with Synthesis Cost Guidance (C) revisits the scaffolds drastically more often than SCENT without Synthesis Cost Guidance, confirming its increased exploitativeness. While this behavior promotes the dis- covery of high-reward scaffolds, it can reduce the diver- sity of discovered modes in certain settings, as observed in Appendix To address this trade-off, we introduced the Exploitation Penalty in Section 3.3 which drastically increases with the number of discovered modes and suc-
0.05
1000
2008
3000
4000
5000
Number of Iterrations
Figure 5: Revisit frequency of high- reward scaffolds increases sharply with Synthesis Cost Guidance (C), indicating greater exploitative behavior. Introduc- ing the Exploitation Penalty (P) effec- tively reduces this revisit ratio.
cessfully limits scaffolds revisits in Figure

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average paths longth
average price of used fragments
c) average number of unique fragments
Frregments costs histogram
SUnioque frogm iommmmnt
Number fragmentt
1002
1 10
SCDVT 10
300
600
Number Ittrationn
Number Rorations
Number Iterations
Number of pattss
Figure 4: Synthesis Cost Guidance reduces trajectory length (a), fragments cost (b), and reliance expensive fragments (d), while concentrating on a smaller fragment subset (c). Results are smoothed the last 100 iterations.
a) Discovered scaffolds (sEH proxy
b) Discovered modes (sEH proxy 8)
Number of scaffolds
Number of modes
20000
SCENT (w/o
SCENT (C)
150000
SCENT (C+
40000
20000
5000
2k
4k
8k
16k
32k
64k
128k
2k
4k
8k
16k
32k
64k
128k
Number of building blocks
Number of building blacks
Figure 6: Number of discovered a) scaffolds and b) modes as a function of the size of building block
library M. The numbers reported for the 3000th training iteration.
4.5 Synthesis Cost Guidance Helps in Scaling
We conducted additional experiments to assess how the size of the building block library M affects the performance gap between SCENT, SCENT without Cost Guidance, and SCENT with the Exploitation Penalty. As shown in Figure 6 Synthesis Cost Guidance consistently increases the number of discovered scaffolds across all library sizes (2k-128k). For mode discovery, the gap widens up to M 64k, then narrows at 128k.
Although initially counterintuitive that Cost Guidance improves mode-seeking, Section 4.3 suggests that it enables more efficient navigation of fragment space, reducing the number of fragments used. Previous work (9] [
shows that reducing library size via random cropping can help, but the effect modest compared our gains. Similarly, removing high-cost fragments improves diversity only marginally (Appendix J.2.2). These findings support our conclusion that Cost Guidance learns to prioritize fragments that are both cost-efficient and reward-relevant.
4.6 Senthesibility-Oriente Metrics
with leading GFlowNer baselines for molecular generation: RGFN and fragment-based GFlowNet (FGFN) from Bengio et al. (7]. We also evaluate FGFN (SA), trained to jointly optimize the sEH proxy and the Synthetic Accessibility (SA) score. As shown in Figure 7 template-based models substantially improve synthesizability. This is most evident in the AiZynthFinder success rate [48). where FGFN fails completely and FGFN (SA) performs poorly relative to RGFN and SCENT. Notably, although FGFN (SA) achieves the lowest average SA score, it still struggles AiZynthFinder, underscoring the limitations of heuristic synthesizability metrics. SCENT achieves the highest average reward and outperforms RGFN in synthesizability. which we attribute to its preference for shorter reaction pathways (Section 4.3). resulting in reduced molecular weight that correlates with the synthesibility metrics (49
We compare SCENT
Conclusion
In this paper, we presented SCENT, a template-based approach for molecular generation within the GFlowNet framework. SCENT introduces three new mechanisms that collectively enhance explo- ration of chemical space: (1) Resursive Cost Guidance, a general strategy for guiding GFlowNet's backward policy, which utilized to improve computational efficiency by removing costly par-

---

SEH Proxy
Molecular Weight
5A Score
AlZynth Success
28
0-
20087 K02
100%
502m7
FO7
530m
607
100%
701
ECENT
10%
10%
Figure Synthesibility metrics for GFlowNet models. SCENT and RGEN were trained on SMALL.
ent state computation. and to focus sampling on molecules with low synthesis cost, (2) Dynamic with smaller fragment libraries-ann and enable the generation of non-linear synthesis trees, and (3) an Exploitation Penalty, which avoids repeated visits to previously explored candidates. Through empirical evaluation, we demonstrated that combining these strategies leads to state-of ofethh-rrt results in template-based molec- ular generation across diverse experimental settings, from small-scale fragment libraries suitable for rapid, automated synthesis to large-scale libraries that create vast search spaces.
Library building blocks, which improve exploration Seficiency-sppeccin
6 Limitations
While our study demonstrates the effectiveness of SCENT in generating cost-efficient and synthesiz- able molecules, several limitations remain.
First, the synthesis cost estimates simplified and heuristic, omitting factors such solvent usage, purification steps, hazardous reagents, reaction conditions, and scalability. Consequently, the current cost model should be regarded first-order approximation. More accurate estimation would require lab-specific calibration, including robust yield prediction models and vendor-specific building block pricing.
SCENT's performance varies across different biological targets, indicating that its effectiveness may depend task-specific factors. Identifying and addressing these bottlenecks is important direction for future work. Key areas of improvement include refining the exploitation behavior of the Dynamic Library, and enhancing the generalizability of the cost predictor model used in Synthesis Cost Guidance. Additionally, we do not assess performance as the number of reaction templates increases, an important scalability consideration, as this expansion significantly enlarges the action space and introduces additional learning challenges.
Our framework emphasizes synthesizability and cost, but does not incorporate toxicity broader ADMET properties, which are critical for downstream drug development. Finally, we acknowledge the absence of wet-lab validation, which remains the definitive benchmark for evaluating the practical utility of our approach. We leave this to future work.
Acknowledgements
The research of P. Gaiński supported by by the National Science Centre (Poland), grant 2022/45/BBST6600117, O. Boussif was supported by the National Research Council Canada (NRC) AI4D program. We gratefully acknowledge Polish nigh-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within PLG//024/017716 The research was also enabled by computational resources provided by the Digital Research Alliance of Canada (https:/ htttpa://allaacceeaea
computational grant
aalliannecann. ca) and
Mila (https://ni :/mmi///mmmmmammm limia..o quebec).. We thank Marcin Sendera and Moksh Jain for their support during the early stages of developing this paper.
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A SCENT Details
A.1 Pseudocode
Algorithm 1 SCENT training
Input: building block library M, reactant patterns Rt, forward policy Pr, flags: C, P. and D.
Initialize: sampling policy Pe. backward policy Pn.
if C then: PB = P x P. else: PB = PB
if P then: Py- Pr PE, else: P Pr with e-greedy.
for i 1 to do
Sample forward trajectories T' from 1%
Sample replay trajectories T'B from prioritized replay buffer using PB.
Update PF minimize LTB over T = TTB.
Update Decomposability Guidance model ènz
if C then: Update Synthesis Cost Guidance model c else: do nothing.
if then: Update Exploitation Penalty counter n(s, a), else: do nothing.
if' D then: Update Dynamic Library utility U and every I steps update M, else: do nothing.
end for
A.2 Forward Policy
The backbone of SCENT's PF is adapted graph transformer model f from [50]. It takes input a partially constructed molecule m (represented as a graph) and an optional reaction template r (represented one-hot vector), and outputs an embedding f(m,r) E R?. The building blocks from M are embedded using a linear combination the ECFP4 fingerprint e(m) and a learnable embedding um) uniquely associated with given building block: h(m) = We(m) v(m).
molecule (and a corresponding synthesis pathway) is composed in a sequence of stages that are further described in this section. All stages linearly transform the state embeddings obtained with backbone model using weight matrices W;.
Select an initial building block. In SCENT, the trajectory starts with a fragment m sampled from M with probability:
p(m.) o, M (I), 4 o (W (W, h(m)),
(14)
where ø is GELU activation function 51 and o* is standard softmax over the logits vector 1 IR* of length k:
01 exp(4)
Σラ exp(1,)
Select the reaction template. The next step is to sample reaction E R that can be applied to the molecule m.
p(ri(m) = of |1+ (I), = (m, ())))))))
(15)
where v(r;) is a learnable embedding for the i-th reaction. Choosing the stop action with the index |R| + ends the trajectory.
Select set of reactants We want to find a building block m, E M that will react with m and reactants M by the reaction template The probability of selecting m, is:
p(m.lm,r, M³) (1)(())) = (WWg((m, (m, h())))(m.)))))
g(m, r, M¹) f(m.r) Σ (h(m()+ +()),
MMMM
where ei) is a learnable positional embedding for the i-th position in the reaction template, the reaction does not require any additional reactant, this step is skipped.
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Perform the reaction and select one of the resulting molecules. In this step, we apply the reaction r to a set of chosen fragments can be empty). We then sample the outcome molecule from set of possible outcomes O:
p(m:) o! 4 =
(16)
where w IR is learnable parameter.
A.3 Cost Prediction Model Training
We use cost prediction models in SCENT: synthesis cost prediction model èg and decomposabil- ity prediction model ég. The size of mini-batches the dataset size N, and the negative sampling ratio were manually chosen to minimize the cost prediction validation losses gathered during the training.
A.3.1 Synthesis Cost Model
The cost prediction model ct is a simple multi-layer perception on top of ECFP4 fingerprint c(s) 20048
E
a(s) W W1d(Wee())), W1 E RIx128
W2 E IR 128x2018
(17)
where ø is the GELU activation function [T].
We train the model cg using the procedure described in Section 3.2 synthesis cost estimates D, we perform five mini mini-batch updates of è using mini-batches of size 1024, the learning rate of 0.01 and the mean square error loss Loost Ex. llc, - 1 where D' is a mini-batch of D. To stabilize training, we standardize all costs D using the mean and variance of costs from the building block library M.
Given an updated dataset with
A.3.2 Decomposability Model
The decomposability model è is very similar to the synthesis cost model, except that it encodes the current number of reactions used in the molecule s. The "cost" in the context of decomposability is 0 if the given molecule can be decomposed into building blocks from M (within some number of steps) and 1 otherwise. In practice, we train the model 6 to predict the decomposability label (0. 1} using binary eross entropy Loost. The labels are gathered similarly to the synthesis cost values, using the trajectories encountered in the GFlowNet forward sampling. We update d(s) with five minibatches of size 1024 and learning rate of 0.005. In addition, we ensure that at least 20% of the samples of molecules for training d(() are not decomposable.
B Training Details
All the models sampled 320,000 forward trajectories during the training in total in SMALL and MEDIUM settings and 256,000 in LARGE. All forward policies were trained using the Trajectory Balance objective (31) and their parameters were optimized using Adam [52]. All methods were trained using their built-in action embedding mechanisms and on a maximum number reactions chosen semi-manually using small grid searches maximize the number of high-reward modes in the SMALL setting in the sEH proxy.
equal 4. Hyperparameters
B.1 SCENT
We based the SCENT implementation on the official RGFN codebase (https://github com/ PeeiiarskilbbRRGFF We followed most of the hyperparameter choices from the official imple- mentation of RGFN [8] We set the number of sampled forward trajectories to 64, and the number of trajectories sampled from the prioritized replay buffer to 32. We set the 3 to 8 for sEH, and 48 for GSK33 and JNK3. We used uniform é-greedy exploration with e = 0.05 for all our experiments, except for those with other exploration techniques (e.g. Exploitation Penalty).
All SCENT instances were trained using Decomposability Guidance, either solely or with Synthesis Cost Guidance using Equation
16

---

Decomposability Guidance
To predict the decomposability of the molecule, we use the model
described in Appendix A.3 We set the cost temperature (Equation (T) to a 5.
Synthesis Cost Guidance The model for Synthesis Cost Guidance is described in Appendix A.3 We set the cost temperature to a
Dynamic Building Block Library We update the dynamic building block library every T = 1000 iterations for maximum of Nasa = 10 additions (that is, we do this until the end of training). The number of molecules added to the library every time it is updated is I. 400.
Equation [2) and schedule y in two ways: 1) so that linearly decays to zero after N iterations. and 2) it grows with the trajectory length (each step increases it by some constant factor Ay). We set N oe for SCENT that uses Dynamic Library and N = 1000 for the rest of the runs. The growing delta Ay 0.2, while the initial so temperature To 1.0. Note that for JNK3 in the MEDIUM setting, we used 4500 iterations for exploitation penalty.
Exploitation Penalty We set
B.2 RGFN
We use the official implementation of RGFN [8] with mostly default parameters. We changed the number of sampled forward trajectories 64, and the number of trajectories sampled from the prioritized replay buffer to 32. We set the 3 to 8 for sEH, and 48 for GSK33 and JNK3.
B.3 SynFlowNet
We followed the setting from the paper 99] and the official repository. We used MaxLikelihood or REINFORCE backward policy with disabled or enabled replay buffer. We grid searched GFlowNet reward temperature 3: (32, 64) for sEH and 3 E {16, 32} for GSK333 and JNK3.
We trained SynFlowNet using 32 forward trajectories and 8000 iterations on LARGE setting, and 64 forward trajectories and 5000 iterations for SMALL and MEDIUM.
B.4 RxnFlow
We used the default parameters from the official implementation and adapted 1) the action_subsampll subsampl intiipiiinaraiooooooooooooo so that the number sampled actions is close to the number used in their paper, and 2) GFlowNet temperatures. We action_ action_sussappl subsampling_rataee ratio=1....... for the SMALL setting, 0.2 for MEDIUM, and 0.1 for LARGE. We set the 3 sampling distribution to Uniform 16, 64) for sEH, and Uniform(16, 48) for GSK33 and JNK3.
action
C Hyperparameters Sensitivity
To assess SCENT (C+D+P) sensitivity to hyperparameters, we performed an ablation study on sEH MEDIUM, varying each hyperparameter around the chosen values (center in tables) over 3k iterations. Results Table 2 indicate SCENT is generally robust, with no catastrophic performance drops. However, hyperparameters notably affect outcomes; for example, simple tuning of ap boost discovered modes by 20%. This highlights potential for further improvements through targeted tuning, left for future work.
D Fragment analysis
In this section, we showcase characteristies of the fragments we used this paper for settings. focus on molecular weight, lipophilicity (logP), the number of Hydrogen bond donors (HBD) and acceptors (HBA), the Topological polar surface area (TPSA) and the number of rotatable bonds (See Figure 8)
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Table 2: Sensitivity of SCENT (C+D+P) to hyperparameters, Each hyperparameter was varied around the chosen values (center in tables). The results are reported for MEDIUM settings after 3k training iterations.
online discovery
inference sampling
model
modes > 8.0 scaff. 8.0
reward f
cost 8.0
Synthesis Cost Guidance (temperature ois)
0,
16036 = 1153
22292 + 1652
7.55 + 0.05
1300 + 82
0ş
16373 1827
39721 = 3574
7.61 0.03
1163 = 81
as 10
13566 + 4207
73916 + 2516
7.87 = 0.04
898 = 42
Decomposability Guidance (temperature o:p)
Q 2
20023 1484
45483 = 2490
7.74 + 0.04
1137 130
a = 5
16373 = 1827
39721 = 3574
7.61 + 0.03
1163 + 81
an 10
20444 2055
44938 # 3111
7.74 0.08
1141 = 80
Dynamic Library (number of added molecules L)
= 200
19700 = 1919
49119 = 6425
7.72 - 0.05
1087 = 183
L, 400
16373 1827
39721 = 3574
7.61 0.03
1163 = 81
= 800
18373 987
48016 = 4302
7.71 + 0.08
1060 = 66
Dynamic Library (frequency of updates T)
T 500
15670 # 2482
39701 = 3676
7.66 0,12
1130 = 106
= 1000
16373 = 1827
39721 = 3574
7.61 - 0.03
1163 = 81
T = 2000
16148 = 1753
39099 = 3341
7.66 0.08
1141 = 82
Exploitation Penalty (initial %0)
Yo = 0.5
20690 = 1512
47220 + 3676
7.69 0.06
1154 + 48
To 1.0
16373 = 1827
39721 = 3574
7.61 0.03
1163 = 81
To = 2.0
20655 = 1514
45990 = 1593
7.75 0.04
1111 + 77
Exploitation Penalty (Ar)
A 0.0
20025 = 762
43432 = 796
7.75 + 0.02
1222 + 65
ÀT 0.2
16373 = 1827
39721 = 3574
7.61 + 0.03
1163 = 81
A 0.4
17189 # 2234
42718 # 2799
7.69 0.04
1108 = 165
0.5
3.3
M2DIUM
ME36LM
MEDIUM Lakge
1.80
0.015
0.3
5100
0.1
8.00
503
1000
3.0
80
50
20
0.000
100
200
10
20
Figure 8: Exploratory analysis of chemical properties of fragments in the SMALL, MEDIUM and LARGE settings.
E Reaction Yields and Fragments Costs Estimates
SMALL setting. The reaction set for the SMALL setting is the extended reaction set from Koziarski et al. (8) We added Bishler-Napieralski and Pictet-Spengler reactions, along with various aryl functionalizations, benzoxazole, benzimidazole, and benzothiazole formation reactions, Hantzsch thiazole synthesis, alkylation of aromatic nitrogen, and Williamson ether synthesis.
Building block prices for SMALL were obtained from the vendors online. multiple vendors offered the same building block, only the cost of the cheapest option was considered. For compounds available in varying amounts, both the price per gram and the alternative size (e.g., 5, 10, 25 grams) were considered, and the smallest price per mmol was used. Compounds that were exclusively accessible
18

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in other forms, such as salts, related functional groups, containing a protected functional group were used with the SMILES fitted to the corresponding reactions. The cost of a product includes the total cost of the building blocks used, while additional expenses such as solvents, catalysts, and reagents are not accounted for. For the MEDIUM and LARGE settings, stock prices were obtained by automatically scraping Enamine's ' publicly available catalog.
For reactions where sufficient in-house experimental data was available (amide coupling, nucleophilic additions to isocyanates, Suzuki reaction, Buchwald-Hartwig coupling, Sonogashira coupling, and azide alkyne cycloaddition), yields were calculated as the average of all recorded experimental yields corresponding to each SMARTS- baseed reaction template. For the remaining reactions in the dataset, yield estimates were derived from the average of reported literature yields for reactions matching the respective SMARTS templates.
MEDIUM and LARGE setting. We used Enamine building blocks subsets of size 64,000 for MEDIUM and 128 128,000 for LARGE settings. The Enamine REAL database was first filtered to retain only molecules with currently available cost information. Fragment selection was then performed randomly for LARGE setting, without any specific criteria, to ensure broad coverage of molecular space. The building blocks for MEDIUM setting were subsampled from LARGE one.
F
Extended Comparison to Template-Based GFlowNets
F.1
Results on sEH with Reward Threshold 7.2
Despite tuning the SynFlowNet [9] parameters (Appendix B). we could not obtain satisfying results. Importantly, our experimental setting differs from the one used in their paper in the following way: we use up to 4 reactions, they use 2 or 3, 2) they use a 10k subset of Enamine, our MEDIUM setting contains 64k molecules, 3) set the reward thresholds higher (for sEH we set 8.0 instead of 7.2), and 4) we train the model for fewer trajectories. In Table 3 we report the results for sEH using threshold from the SynFlowNet paper. The number of modes obtained seems consistent with those reported in Cretu et al. 9
F.2 Results on GSK33
We benchmark SCENT on the GSK3,8 proxy and observe that approach consistently discovers more modes and high-reward scaffolds than alternative reaction-based GFlowNets (see Table 4). The exploitation penalty improves performance across all settings, while using cost biasing alone (SCENT (C)) is slightly outperformed by SCENT (w/o C), which does not use cost biasing. However, it worth noting that SCENT (C) finds cheaper molecules, with the fully enabled SCENT (C+P) identifying the cheapest ones. In the largest setting, cost biasing increases the number of modes found, and adding the exploitation penalty further boosts performance.
F.3 Results on JNK3
In Table 5 RGFN outperforms all SCENT variants in the smallest setting, consistently finding more diverse modes and high-reward scaffolds. While SCENT lags behind in terms of mode discovery, compensates in terms of cost, since SCENT (C) samples the cheapest molecules. In the MEDIUM setting, SCENT (w/o C) outperforms the baseline methods and the fully enabled SCENT variants in terms of mode discovery, but lags behind in the number of high-reward scaffolds. That can be attributed to the fact that SCENT (C) samples are less diverse. However, SCENT (C+P) samples the cheapest molecules. Finally, in the largest setting, the fully enabled SCENT (C+P) finds the most number of modes and begh-reward scaffolds, whereas SCENT (C) takes the spotlight in terms of cost, generating the cheapest molecules.
F.4 Comparison of computational resources
In Table 6 report GPU runtimes (in minutes) for all template-based baselines on the sEH proxy, averaged over 3 random seeds using V100 32GB GPU. All methods used a batch size of 64, except SynFlowNet, which required batch size 32 on the LARGE setting even after optimizing its policy code to fit within memory limits.
19

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Table 3: Evaluation of different template-bas based approaches using sEH proxy in SMALL, MEDIUM
and LARGE settings, but using lower reward threshold (> 7.2) equivalent to the one used in SynFlowNet paper 9
online discovery
inference sampling
model
modes 7.2 f
scaff. > 7.2 t
reward
cost >> 7.2
RGFN
11320 166
76742 =693
7.34 10.06
35.3 11.6
SynFlowNet
559 =217
4795 +2477
6.89 +0.29
22.6 +7.3
RxnFlow
472 #259
734 4:270
4.18 #0.72
52,3 #5.9
SMALL
SCENT (w/o C)
13862 +:422
114522 =1558
7.38 =0.02
38,8 +1.0
SCENT (C)
11978 199
108262 11355
7.43 #0.02
23.5 +2,8
SCENT (C+D)
11877 1:48
110522
11322
7.56
10.05
18.9 1:28
SCENT (C+P)
14399 11149
137443
1880
7.42
10.05
19.5 11.
SCENT (C+D+P)
14631 206
137426
1055
7.66
10.03
20.9 11.7
RGFN
45937 13008
77876
1:3078
7.08 10008
1386 1:64
MEDIUM
SynFlowNet
32310 +:682
35481
-463
6.38 +0.03
1666 109
RxnFlow
2458 1:46
2613 4445
6.3 10002
1645 1:56
SCENT (w/o C)
126409 4440
141284
22833
7.31 10.09
1.594 1:59
SCENT (C)
108921 17019
212402
1361
7.74 10004
1151 1:14
SCENT (C+D)
104702 #3536
213750
11818
7.71 #0.03
1150 +0
SCENT (C+P)
158578 3361
277811
44383
7.81
0004
1230 1132
SCENT (C+D+P)
144224 #2329
274380
1388
7.75 #0.08
1096 +:29
RGFN
54255 1573
59210 1146
7.09 #0.11
1766 +:50
SynFlowNet
20186 + 13305
36656 222657
6.39 10.19
1545 +:70
RxnFlow
1615 1:60
1708 67
6.14 +0.08
1800 4:22
LARGE
SCENT (w/o C)
95457 11292
104789 =1684
7.13
=0.12
1776 +:50
SCENT (C)
83729 11157
157331
=1783
7.52 +0.09
1230 1:15
SCENT (C+D)
90133 13366
164962
12931
7.68
10.11
1267 1116
SCENT (C+P)
140654 16121
198503
15447
7.76
10003
1250 1661
SCENT (C+D+P)
136677 13733
199216
5986
7.78
10.11
1362 146
RGFN shares a part of the codebase with SCENT, allowing for direct comparison. We observe consis- tent improvements across settings, particularly in the SMALL configuration. While SynFlowNet is faster on SMALL and MEDIUM, it struggles on LARGE, even with our memory optimizations. However, differences in codebases make direct comparisons less definitive.
Note that our implementation is not optimized for runtime and could benefit from further improve- ments.
G Qualitative Pathways Analysis
To visualize the Synthesis Cost Guidance in practice, we gathered training trajectories from SCENT (C) and SCENT (w/o C) that lead to the same high-rewarded molecule in the SMALL setting.
We observed that, on average, cost-guided trajectories are 10% cheaper. We selected trajectory pairs and noticed that the cost reduction usually occurs by changing the order of the reactions: introducing expensive fragments later in the synthesis path, which decreases the product losses due to imperfect yields, and as result increases the cost-efficiency. In Appendix G SCENT (C) chooses the most expensive building block in the sequence ($12.56) at the beginning of the synthesis, while SCENT (C) utilizes it in the second step, effectively reducing the synthesis cost by almost 28%. Cost-guided model also occasionally prefers cheaper fragments, e.g.. "Brelc[nH]ent instead of "Ielc[nH]en1" It is worth emphasizing that the fragments in the SMALL setting already low cost, which is the reason why most gains are observed due to reaction order swapping.
example
Similarly, we compared the Dynamic Library, SCENT (C+D), to SCENT (C), and observed that on average the trajectories are 12% shorter and 6% cheaper. We selected few examples that visualized the compressed trajectories. We have found that the Dynamic Library enables the generation of
20

---

Table 4: Evaluation of different template-based approaches using GSK33 proxy in SMALL, MEDIUM and LARGE settings. In all metrics, except "reward", we consider molecules with reward > 0.8. We observe that SCENT (C) that uses Cost Guidance greatly reduce the cost of the sampled molecules, while drastically increasing the number of scaffolds and modes discovered during the training (in MEDIUM and LARGE settings). Dynamic Library (C+D) significantly improves the performance of the model in the SMALL setting. Using exploitation penalty (+P) further boosts the results, making SCENT by far the most powerful evaluated method in all settings.
online discovery
inference sampling
model
modes 0.8 f
scaff. > 0.8 f
reward
cost > 0.8 4
RGFN
285 144
1870 100
0.7 +0001
42.3 +2.7
SynFlowNet
0.0 10.0
0.0 1000
0.57 10.01
RxnFlow
2.0 10.0
2.0 10.0
0.51 10.05
SCENT (w/o C)
290
1911
0.7 10001
44.1 1:5.1
SMALL
SCENT (C)
287 #9
1856 147
0.7 =0.0
34,9 +4.5
SCENT (D)
319
2196
0.71 10.01
38.0 11.
SCENT (P)
330 16
2447 99
0.7 #0,01
45.8 11.8
SCENT (C+D)
306 11
2138 25
0.73 =0.01
30.3 +1.
SCENT (C+P)
294 18
2000 1009
0.71 #0.0
30.0 #0.4
SCENT (C+D+P)
336
2172 =92
0.72 #0.01
32.1 #3.1
RGFN
4037 1278
5225 1122
0.64 #0001
1251 #211
SynFlowNet
681 1184
1015 +393
0.58 #0.02
1565 #415
RxnFlow
64.0 11488
88.0 11.2
0.58 10.0
1602 1:59
MEDIUM
SCENT (w/o C)
6430 +:427
10010 +:257
0.66 10.03
1177 1117
SCENT (C)
5945 44009
50435 13360
0.74 10.3
917 2000
SCENT (D)
5668 #2149
8548 2297
0.64 1002
1217 1:90
SCENT (P)
6494 11812
11493 +: 1929
0.66 10.02
1164 1:19
SCENT (C+D)
9430 14484
64721 118868
0.77 10.01
776 247
SCENT (C+P)
8868
1: 1883
92813
134174
0.77 10.05
815 1.56
SCENT (C+D+P)
11857 44171
93761 +33462
0.82 -0.04
730 1184
RGFN
1748 1660
2056 1638
0.62 10.02
1370 1176
SynFlowNet
402 -237
970 6687
0.53 #0.01
RxnFlow
31.7 45.6
37.7 +5.0
0.55 #0.0
SCENT (w/o C)
3366 +3503
4873 14494
0.6 #0.07
1842 1:606
LARGE
SCENT (C)
4295 1468
28867 12418
0.7 +0.01
973 119
SCENT (D)
2256 4472
3185 5514
0.57 10.03
1390 223
SCENT (P)
1855 234
2604 #314
0.6 =0.0
1376 1:68
SCENT (C+D)
8238 1.716
37957
1.3277
0.74 10.01
693
SCENT (C+P)
7890 18669
26553
1:2121
0.73 10.01
931 197
SCENT (C+D+P)
5574 708
34595
1:3254
0.73 10.03
990 151
convergent synthetic pathways (Appendix(G), which responsible for the reduction of the synthesis cost. The application of the convergent synthetic strategy decreases the longest linear sequence (LLS) of steps, thereby increasing the overall yield.
In the MEDIUM settings, the SCENT variations diverged early on, so the set shared molecules is very limited. Therefore, we matched high-rewarded molecules from SCENT (C) to closest high- rewarded molecule from SCENT (w/o C) with Tanimoto similarity > 0.6. We observed that, on average, the cost-guided trajectories are 20% cheaper. Thorough investigation of few selected trajectories pairs revealed that, differently from SMALL setting, in MEDIUM setting, the cost reduction comes mostly from the selection of cheaper fragments (Appendix G
H Extended Synthesis Cost Guidance Analysis
In this section, we extend the analysis from Section with results in the SMALL setting. Figure shows that similarly to the MEDIUM setting, Synthesis Cost Guidance reduces the average length of
21

---

SCENT w/o C
Yiet: TOLES
$25
75.0%
$3.30
$25.00
$33
$3.19
$33.60
(b) SCENT
TARA: YOLPH:
75
$3.00
$27
$3.30
$17.60
$24
$3.19
124.86 S6
(c) SCENT C+D
Ylald: 75.0%
75.6%
$3.19
$24.55
$32
Figure 9: Three example pathways from SMALL setting leading the same molecule. SCENT (C)
is cheaper than SCENT (w/o C) by introducing the costly fragment (S12.56) later in the pathway.
22

---

(a) SCENT w/o C
Resk 7576
$2597
$35.00
257.04
S76
(b) SCENT C
Seld 75.%
30.33
TeR 75.5%
$29.0
$55
$57.34
(c) SCENT C+D
Yeat 752%
10.22
16.50
66.6
Tel 75.%
$25.04
$22
$65.22
Figure 10: Three example pathways from SMALL setting leading to the same molecule. SCENT (C) is cheaper than SCENT (w/o C) by introducing the costly fragment ($16,81) later in the pathway. SCENT (C+D) further lowers cost via convergent strategy that shortens the longest linear sequence (LLS), boosting overall yield.
23

---

Table 5: Evaluation of different template-bart approaches using JNK3 proxy in SMALL, MEDIUM and LARGE settings. In all metrics, except "reward", we consider molecules with reward 0.8. We observe that SCENT (C) that uses Cost Guidance can greatly reduce the of the sampled molecules in SMALL and MEDIUM settings, while drastically increasing the number of scaffolds.
online discovery
inference sampling
model
modes >> 0.8 ?
scaff. > 0.8 1
reward
cost >> 0.8 y
RGFN
307
2852 442
0.8 =0.0
11.6 +0.3
SynFlowNet
0.0 =0.0
0.0 =0.0
0.42 #0.88
RxnFlow
16.3 +1.
70.3 +4.1
0.69 #0.0
12.2 #0.2
SMALL ILL
SCENT (w/o C)
271 #9
2363 1000
0.78 #0.01
16.8 +2,8
SCENT (C)
256
2248
0.8 10.01
9.27 10.5
SCENT (D)
287 19
2708 3338
0.78 10.0
11.7 1006
SCENT (P)
264
2441
0.79 10.01
14.2 10.7
SCENT (C+D)
293 #1
2771 =38
0.79 #0.0
8.82 10036
SCENT (C+P)
247
2395
0.8 10002
12.0 +3.4
SCENT (C+D+P)
303 16
2765 150
0.8 10.0
9,45 20.55
RGFN
737 62
5685 1.999
0.6 10001
1699 183
SynFlowNet
0.33 10.47
0.33 20.47
0.5 10.00
RxnFlow
4.0 +1.0
4.0 =10
0.51 #0.01
MEDIUM
SCENT (w/o C)
711 775
11383 #5011
0.62 #0.03
1599 #248
SCENT (C)
525 =61
51264 23350
0.77 #0.02
1197 =320
SCENT (D)
875 1138
13497 +: 1009
0.65 10.0
1468 1:333
SCENT (P)
836 1133
13778 1:352
0.65 10.11
1539 1:14
SCENT (C+D)
423 1008
60061 131036
0.81 10.01
1063 1:352
SCENT (C+P)
637
91872 5:542
0.76 10.01
869 1:38
SCENT (C+D+P)
645 1330
89508 4:53412
0.79 10006
1909 1:395
RGFN
203 1:7
305 14
0.63 10.02
1652 1.59
SynFlowNet
5.0 17.07
83.3 117.9
0.51 10.1
RxnFlow
3.0 11663
3.67 2.49
0.5 10.01
SCENT (w/o C)
430 1:255
1817 11219
0.58 1.001
2454 1637
LARGE
SCENT (C)
116 =44
3165 2443
0.67 #0.02
1213 186
SCENT (D)
112 1
502 4:44
0.57 +0.02
SCENT (P)
99 1.935
536 1298
0.57 10.03
2531 .1563
SCENT (C+D)
113 #87
10412 #5619
0.71 10.01
1403 ::405
SCENT (C+P)
442 +325
15192 19772
0.71 10.4
1383 122
SCENT (C+D+P)
472 +238
11616 #5744
0.71 10.0
1278 1:43
Table 6: GPU runtimes (in minutes) for all template- handlate-based baselines the sEH proxy, averaged over 3 random seeds using a V100 32GB GPU.
Model
SMALL
MEDIUM
LARGE
RGFN
2882 + 115
3892 + 153
4455 + 392
SynFlowNet
200 +
2350 162
8416 + 19
RxnFlow
891 +
3304
3399 = 50
SCENT (C+D+P)
800 + 15
2849 144
3688 + 317
the trajectories and the average price of the fragments used also in the SMALL setting, Interestingly, does not significantly limit the usage of fragments, which is likely due their shortage and relatively uniform cost. We also see that the reaction yield does not play a role in reducing the cost.
I Further Comparison to Baselines
In this section, we extend our comparison of SCENT to established baseline models from the literature: REINVENT 20 and Graph GA [53] Figure B shows comparative analysis of the

---

(a) SCENT w/o C
Yield: 75.0%
$125.31
$279.35
$539.55
(b) SCENT C
$125.37 31
$27.27
$201.45
Figure 11: Two example pathways leading to structurally similar high-rewarded molecules. A molecule proposed by SCENT (C) uses much cheaper fragment ($27.27 $279.35).
paths length
fragments
raccion ylald
3.44
10
12.2
0,750
Vieid
5.30
2.30
0,740
1003
M000
5000 3200 3000
5000
1205
2026
2000
5000
iterations
Figure 12: Cost reduction analysis for sEH proxy and SMALL setting. Cost Guidance decreases the average length of the sampled trajectory (a), and the average cost of used fragments (b) which directly reduces the synthesis path cost. Importantly, the average reaction yield (d) does not increase when using Cost Guidance, suggesting its low influence in reducing the synthesis costs. The plots are smoothened by averaging results from the last 100 iterations.
molecular properties generated by each method, filtered through AlZynthFinder 48 retain only retrosyathetically accessible compounds. SCENT achieves a reward comparable to REINVENT. while outperforming it in terms of diversity and novelty. Graph GA generates highly diverse molecules, but these are often challenging to synthesize, as indicated by its low support the final sub-plot. Importantly, both SCENT and REINVENT lie the Pareto front of the reward-novelty trade-off: SCENT is biased toward higher novelty, whereas REINVENT tends toward higher reward. Table [ and 8 further support this comparison, confirming that SCENT generates diverse, high-quality, and synthetically accessible molecules.
zEH Praxy
Diversily
Novelty
Reward Novelly Tradcoff
acaet (oote
Q
Novely
50ONT
RENNCT
SCENT
RERGENT
SCENT
RC//CNT
7.3
Reward
Figure 13: Comparison of molecules sampled by SCENT, REINVENT, and Graph GA, filtered using AIZynthFinder to retain only synthetically accessible candidates. Diversity is measured as the average Tanimoto distance between molecule and the rest of the sampled set, while novelty is defined as the average Tanimoto distance the closest molecule in the ChEMBL database. SCENT the SMALL setting. We observe that it generates diverse, high-reward, and synthesizable molecules, and lies the Pareto front of the novelty-reward trade-off. Tick marks in the final sub-plots indicate the mean values of the sampled distributions.
trained in
25

---

Table 7: Comparison of generative models, Diversity, Novelty, and AlZynth scores were computed on the top 100 highest-scoring scoring molecules. Diversity is defined as the average Tanimoto distance between each molecule and the rest of the sampled set. Novelty refers to the average Tanimoto distance to the closest molecule in the ChEMBL. database. AlZynthh represents the percentage of molecules successfully retrosynthesized by the AIZynthFinder software. All results are averaged over three independent runs of training and evaluation. Template-based models were trained under the SMALL setting.
Setting
Reward
Diversity
Novelty
AlZynth
RGFN
7.34 10.06
0.77 10.02
0.58 10.01
0.647 100037
SynFlowNet
6.89 +0.29
0.74 +0.02
0.53 #0.01
0,247 #0.090
Template-based
RxnFlow
6,30 +0.03
0.80 =0.00
0.53 #0.01
0.653 #0.021
SCENT (C+D+P)
7.66 +0.03
0.73 #0.01
0.59 :0.01
0.773 #00066
Graph GA
7.30 40.31
0.56
0.62 +0.08
0.113 10.160
Unconstrained
FGFN
6.03 +0.10
0.85 10.00
0.66 10000
0.010
10.000
FGFN + SA REINVENT
6.16 +0.20
0,80 1:001
0.54 +0.02
0.453 +0.991
7.97 +0007
0.59 10.10
0.50 +0.05
0.800 10.113
Table 8: Comparison of generative models on Quantitative Estimation of Drug-likeness (QED), weight, Synthetic Complexity (SC), and Synthetic Accessibility (SA) metries. Template-based models were trained on SMALL setting.
Setting
QED f
weight 1
SC
SA
RGFN
0,244 +0.03
542 47
4.84 10001
3.30 +0.03
SynFlowNet
0.454 10.133
451 176
4.42 10.28
3.06
Template-based
RxnFlow SCENT (C+D+P)
0,259 10.00
603 499
3.34 10.04
4.73 40.06
0.286 10.004
519 155
4.82 10.01
3.24 40.02
Graph GA
0.464 10.03
367 1:12
3.72 10.23
3.55 10.66
FGFN
0.197 10.00
668 1.5
4.88 10.01
4.77 10.06
Unconstrained
FGFN + SA REINVENT
0,186 +0.004
621 +10
4.77 +0.04
3.42 #0.12
0.188 +0.088
655 1179
4.64 #0.07
2.76 +0.12
J Ablations
J.1 Decomposability Guidance Ablation
Decomposability Guidance, described in Section 3.2.2 is integral part of our SCENT model. Leveraging a machine lcarning model, it predicts which molecules can be further decomposed in the backward transition (retrosynthesized) and discards the transitions that would otherwise lead to invalid states. Table 9 that this component is crucial for the performance SCENT.
Table 9: Effect of Decomposability guidance on online discovery and inference in SCENT.
online discovery
inference sampling
model
modes 8.0 f
scaff. > 8.0 t
reward
cost >> 8.0 J.
w/o Decomposability
309 .14
2794 1.77
7.3 100003
52.2 1.49
SMALL
w/ Decomposability
510 126
5413 1333
7.38 10.02
37.1 11.0
w/o Decomposability
6989 1:303
8840 1:652
7.15 10.05
1549 1:81
MEDIUM
w/ Decomposability
9310
1163
11478
.1823
7.31 1.009
1463 1.62
26

---

Table 10: The machine learning-based based cost estimation significantly outperforms constant value cost estimation, providing substantial performance boost, particularly in cost reduction.
online discovery
inference sampling
model
modes >> 8.0
scaff. > 8.0 1
reward
cost > 8.0 4
w/o Cost Guidance
510 1:6
5413 =334
7.38
10002
37.1 +1.0
SMALL
Constant ML predicted
515 :9
5259 115
7.43 10.07
45.7 #2.8
478 #:1
5150 1:7
7.43 =0002
23.7 14.0
w/o Cost Guidance
9310 1663
11478 1822
7.31 10.09
1463 1:62
MEDIUM
Constant
13121 1003
25487 =907
7.41 #0.02
1368 +149
ML predicted
17705 4224
52340
=4303
7.74 00.44
1163 1147
w/o Cost Guidance
7171 291
8767
=429
7.13 #0,12
1678 +3
LARGE
Constant 12218 11225
26348
53397
7.44 #0.12
1369 1127
ML predicted
11730 1777
88592
16256
7.84 1000
1014 1337
J.2
Synthesibility Cost Guidance Ablations
J.2.1 Cost Prediction Model Ablation
In this section, we demonstrate the importance of accurately estimating true cost using the machine learning model c' for the optimal performance of SCENT. We compare model with low-quality cost estimator that outputs a constant value for every imput molecule, where this constant is the mean of all the building blocks in the given scenario. It is important to note that only modify the recursive component of the C function from Equation (5). keeping the fragment costs unchanged. As shown in Table 10 the use of constant value can reduce costs and improve SCENT's performance in MEDIUM and LARGE settings. However, machine learning- based cost estimation significantly outperforms this approach, providing substantial performance boost, particularly in cost reduction. From this, we conclude that the quality of the cost prediction model used in Cost Guidance is critical the performance of SCENT.
J.2.2 Comparison of Cost-Red Methods
In this section, compare cost-guided SCENT with other methods to reduce the synthesis cost. The first method is to use the forward synthesis cost as a reward function component (we denote this approach as the "cost reward"). The second method that tested trained on subsets of the building block library M with the top v2% of the most expensive molecules being removed. While both approaches can effectively reduce the cost of proposed molecules, they lag behind cost-guided SCENT in terms of online discovery metrics, as can be seen in Figure [4] This suggests that cost biasing strikes the perfect balance between cost-fffectiveness and diversity is able consistently find more modes.
number of modes
number of igh reward scaffolds
Cost of rewhrredd
50000
top weterokin
guidance
4000
numbbr moodss
15000
40000
Wi 12 5% top expensive
3000
30000
giédenoe
30000
20000
5000
mambaa
0
1000
2000
5000
4000
5000
0
1000
2003
9000
4000
50.00
mothods
feaaton
foration
Figure 14: Plot comparing different approaches reduce the cost of the molecules in MEDIUM setting on sEH proxy. We observe that Cost Guidance discovers by far the largest number of (high- reward) modes and scaffolds while reducing the cost.
27

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J.2.3 Update of Building Block Costs
We conducted an experiment on SCENT (C+D+P) to evaluate system robustness to changes in fragment costs.
At iteration 3000, we randomly reshuffled fragment costs and continued training without resetting model parameters, only updating the costs estimates used to train the cost predictor. Plots showing the evolution of cost and reward over time can be found in Figure 13 As expected, the trajectory cost spiked immediately after the change but quickly decreased, converging to the same level reached by model trained from scratch with the new cost assignments (within 1000 iterations). The conclusion is that SCEN (C+D+P) does require full retraining when costs change. Updating the cost predictor is sufficient.
Wirm-start: standard us shuffled (cost)
Warm-start: standant vs shuffled (reward))
shuffied
aaad
1500
Mesan newardd
co4t
2.3
1200
shuffee
1043 2003
3000
4000
2006
3000
2300
3000
4000
5003
Training 5tep
Training step
Figure 15: The evolution of cost and reward over training iterations. "Standard" refers to the method where costs were shuffled the start of training. "Shuffled" refers to the method where shufiling occurred at the 3000th iteration. Results are reported on 1500 validation trajectories and averaged across 3 random seeds.
J.3 Dynamic Library Ablation
Figure iojluustrates how the performance of the reward-basee bbsed Dynamic Library scales with the size of the initial set of building blocks. The improvement over the baseline (SCENT (C+P)) is substantial when the initial library is small but diminishes as its size increases. We also conduct an ablation study the library-building mechanism, comparing our method- from the reward-sorted buffer-to a variant where L molecules are randomly sampled from the buffer. Our results indicate that the selection mechanism plays crucial role and is the primary driver of performance improvements.
the top T. molecules
Number soaffolcc
40%
Number of modes
60%-
30%
30%
10%-
0%
0%
-10%
fregments
Number fragments
Figure 16: Plot comparing the relative improvement in performance between using reward- based Dynamic Library (SCENT (C+P+D)) as opposed to iniform-based one over the baseline (SCENT (C+P)).
J.4 Exploitation Penalty vs Other Exploration Techniques
In this section, we compare our Exploitation Penalty to the widely used e-greedy exploration where e 0.05. Additionally, we "decay" variants that linearly decrease the exploration parameters (c for e-greedy, and for exploitation penalty) so that they reach 0 at the 1000-th iteration. Figure
28

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with/without Cost Guidance, and with Dynamic Library. We observe that exploitation penalty with linear decaying (penalty decaying) helps our cost guided SCENT the most, successfully mitigating its exploitative nature. Dynamic Library also benefits from using an exploitation penalty, possibly because the penalty explicitly encourages the model to visit newly added molecules. Table [T shows that our Exploitation Penalty helps the cost-guided SCENT in exploration, especially when accompanied with Dynamic Library.
compares the exploration techniques on three variations of SCENT:
modes
w/o cost guidance
cast gui dance
cast guidance dynamic library
ditabled
number ofmodes
number of modes
numbar of modes
uniform decaying
400
penalty
400
400
3000
4000
3000 Itoratoon
2000
3000
Figure 17: Comparison of the exploration techniques on three variations of SCENT: with/without Cost Guidance, and with Dynamic Library. We observe that exploitation penalty with linear decaying (penalty decaying) helps our cost guided SCENT while Dynamic Library benefits significantly from using a non-decaying penalty.
Table 11: Comparison of different exploration techniques for sEH in SMALL setting.
SMALL setting
online discovery
inference sampling
model
modes
8.0
scaff. 8.0 f
reward
>> 8.0 |
w/o cost guidance
disabled
613 +:0
7599 1:33
7.42 #0005
39.3 43.9
uniform
510 +:6
5413 1334
7.38 4000
37.1 :1.0
uniform decaying
589 +12
6880 1:36
7.41 10006
33,2 4:39
penalty
521 +15
3660 199
7.47 #0.02
39.6 #2.1
penalty decaying
615 +16
7603 +:39
7,38 +0002
39.4 +2.4
cost guidance
disabled
509 +24
5794 +337
7.4 +0.08
23.9 #7.6
uniform
478
5150
7.43 +0.02
23.7 14.0
uniform decaying
560 #3
6487 .1299
7,43 100.4
21.6 11.4
penalty
531 1:31
4162 1:279
7.62 1.0.11
26.3 1:4.
penalty decaying
595 1:00
6839 1:360
7.42 1:005
20.7 12.66
cost guidance
+ dyn. library
disabled
555 1:43
7098 1:770
7.56 10.04
20.5 12.66
uniform
596 +19
7148 1:497
7.56 +0.05
19.7 4:200
uniform decaying
599 +:1
7670 #:34
7.49 #0007
21.0 10.9
penalty
715 115
8768 1.33
7.66 10.03
20.7 4.10
penalty decaying
639
8323
7.66 10.01
21.0 11.2
29

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Reviews:
[{"review_id": "LjTfu2dGk8", "reviewer": "Reviewer_9Lb3", "summary": "The authors introduce SCENT, a GFlowNet (GFN)-based framework designed to overcome limitations of prior template-based approaches for synthesizable molecular generation: (1) not considering \"synthesis cost\" (2) weak scalability when dealing with large building-block libraries and (3) poor/inefficient use of small fragments. The paper proposes to tackle these problems by introducing \"Recursive Cost Guidance\", where a learned cost predictor $\\hat c_{B}^{S}$ steers the backward policy toward low-cost intermediates (defined by building block stock-prices and reaction yields) and a decomposability model that prevents invalid retrosynthetic steps. In addition, the authors propose a forward-policy regularizer that penalizes repeated state-action visits to counteract exploitation introduced by the cost guidance (\"Exploitation Penalty\"). Lastly, the authors explore a fragment pool augmentation method where they periodically add high-reward intermediates to the pool, ranked by their expected utility.", "questions": "* What happens if the stock-prices of the building blocks change? Do you need to retrain all modules from scratch, or is the system robust enough to just update the cost predictor and fine-tune? Brief analysis/experiments regarding \"warm-start\" scenario could add value to the draft and its applicability to real-world scenarios.\n* It’s unclear how the “Dynamic Building Block Library” truly expands the space of synthesizable molecules (mentioned in line 198). In principle, any intermediate added to the library was already reachable via longer retrosynthetic trajectories, so the *theoretical* reachable set should remain unchanged. Could the authors clarify what they mean by “expanding” the space versus merely compressing trajectory length?\n* How sensitive is the proposed approach to the Dynamic Library hyperparameters, specifically, the frequency of pool updates $T$ and the number of added intermediates $L$?\n* How are the reaction yield estimates computed for the cost predictor? Are they the average of all available experimental yields per template, the highest reported yield, or some other summary statistic? Clarifying this would help assess the bias of the cost-guidance module.", "limitations": "The authors do not address the limitations of their work in the draft.\n\n- **Unrealistic “one-step” fragments.** The Dynamic Library treats discovered intermediates as if they were freely available building blocks, ignoring the multistep chemistry (and costs) needed to actually make them before use. This should probably be included in the expected utility in some form.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**  \n- The paper is well-written and logically structured, which makes it easy to follow.\n- The authors clearly identify and address key drawbacks of existing template-based GFN approaches for synthesizable molecular generation.\n- Comprehensive ablation study (Table 1, Sections 4.3–4.5, Appendix) help quatify the individual contributions of each proposed component.  \n- **Recursive Cost Guidance** directly integrates synthetic cost considerations into the backward policy rather than treating them solely as a reward signal, resulting in consistent reductions in average building-block price and shorter synthesis paths. This is important since rather than aiming to match the full support of high and low-cost routes/yields, this approach focuses the backward policy on the most synthetically viable, high-reward modes, which aligns directly with the practical objective of discovering cost-efficient molecules.\n- **Exploitation Penalty** effectively counteracts the over-exploitation introduced by cost guidance, as demonstrated by reduced scaffold-revisit rates in Figure 5.\n\n**Weaknesses**\n- Yields are considered to be constant for each template, when these are known to show high variance across reactants, reagents, solvents and conditions. \n- Cost of a product only considers building blocks, ignoring solvents, catalysts or reagents. As a proxy, these values could be integrated in some sort of cost for the templates themselves.\n- No limitations section or future work.\n- No further qualitative analysis on the quality of the generated structures and their corresponding routes. Is there any noticeable introduced bias on the generated routes/structures after adding each of the proposed components?\n- Building-block stock prices fluctuate and depend on purchase scale and vendor. The paper does not explore how often price updates require warm-starting or full retraining, nor quantify the impact of price uncertainty on model outputs.", "was_revised": true, "final_justification": "Considering the rebuttal responses, I maintain my positive recommendation. The paper makes a meaningful contribution to the literature on synthesizable molecular generation by directly integrating cost considerations into the GFlowNet architecture (guidance), rather than treating them solely as a reward signal. The authors’ warm-start study shows robustness to changes in building-block costs through a post-training process, which strengthens the practical applicability of the approach.\n\nTheir clarifications and additional experiments on the Dynamic Library’s behavior, hyperparameter sensitivity, and qualitative pathway analysis addressed the key concerns I raised. While certain limitations remain (e.g., overly simplified yield and cost models), these seem to be reasonable given the scope of the work. Overall, the manuscript is well-written, technically sound, and advances aspects of synthesizable generation methods, particularly in linking cost efficiency with route generation.", "rebuttal": "We sincerely appreciate the reviewer’s thoroughness and insightful suggestions. We have incorporated all of them and believe that the warm-start study and qualitative analysis of the pathways, in particular, have significantly strengthened our paper. Regarding the concerns raised, we have carefully addressed each one and kindly encourage the reviewer to consider this in their evaluation.\n\n## Warm-start study\n\nWe found the “warm-start” scenario compelling and conducted an experiment on SCENT (C+D+P) to evaluate system robustness to changes in fragment costs.\n\nAt iteration 3000, we randomly reshuffled fragment costs and continued training without resetting model parameters, only updating the costs estimates used to train the cost predictor. As expected, the trajectory cost spiked immediately after the change but quickly decreased, converging to the same level reached by a model trained from scratch with the new cost assignments (within ~1000 iterations). This behavior was consistent across three random seeds and both SMALL and MEDIUM settings.\n\n**Conclusion:** The system does *not* require full retraining when costs change. Updating the cost predictor is sufficient. \n\nPlots showing the evolution of cost and reward over time have been added to the revised paper (not included here due to rebuttal format constraints).\n\n## How Dynamic Library expands the reachable set?\n\nIt is true that, by definition, any intermediate molecule is reachable via some trajectory. However, once a molecule is added to the dynamic library, it becomes available as a reactant. This enables synthesis paths (trajectories) to become non-linear, as a reactant can now be a previously synthesized molecule—rather than being limited to the pre-defined reactant pool. This is illustrated in **Figure 3**: the molecule highlighted in red is itself a synthesis tree and could not have been reused as a reactant without the Dynamic Library. More formally, SCENT without the dynamic library is restricted to synthesis trees in which each node has at most one non-leaf child. Under this constraint, not all molecules are reachable. To experimentally validate that, we randomly generated 10000 full synthesis trees (balanced binary trees) using fragments and templates from MEDIUM settings, and 43% of the obtained molecules were observed to be non-decomposable with standard tree-like trajectory, confirming our claim.\n\n## Dynamic Library hyperparameters sensitivity\n\nWe conducted an extensive ablation study to assess the sensitivity of SCENT to hyperparameter choices across all components. Detailed results are provided in the response to Reviewer QKfB (Section *Hyperparameters*). We find that reducing the number of intermediates can slightly improve performance, while changing the update frequency of the dynamic pool has negligible effect. Overall, SCENT exhibits strong robustness to the hyperparameters of the Dynamic Library.\n\n## How are the reaction yields obtained?\n\nThank you for noting the imprecision in Appendix C. For reactions where sufficient in-house experimental data was available (amide coupling, nucleophilic additions to isocyanates, Suzuki reaction, Buchwald–Hartwig coupling, Sonogashira coupling, and azide–alkyne cycloaddition), yields were calculated as the average of all recorded experimental yields corresponding to each SMARTS-based reaction template. For the remaining reactions in the dataset, yield estimates were derived from the average of reported literature yields for reactions matching the respective SMARTS templates.\n\n## Constant yields\n\nWe fully acknowledge the reviewer's concern that our yield assignment method is simplified. However, in our view, this simplification is necessary, as no more accurate and universally applicable yield estimation methods currently exist [1]. Importantly, our cost‑biasing framework is fully modular and can accommodate **any recursively defined cost estimation**, including those with sophisticated, laboratory-specific yield‑prediction models.\n\n[1] “When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges”\n\n## Solvents and reagents are ignored\n\nIncluding solvent and reagent costs in template cost estimation is a valuable suggestion and may be important in practical applications. However, accurately estimating these costs is nontrivial, as they are often lab-specific and less standardized than building block prices. In our work, we focus on building blocks, which typically dominate overall cost, and adopt a simplified approximation that excludes solvents and reagents. While this may reduce accuracy for any specific lab setting, it offers a more robust and broadly applicable estimate.\n\nImportantly, our framework is flexible: more detailed or lab-specific cost models, including those that incorporate reagent pricing, can be readily integrated. We view this as a promising direction for future work.\n\n## Pathways Analysis\n\nTo visualize the cost guidance in practice, we gathered training trajectories from SCENT (C) and SCENT (w/o C) that lead to the same high-rewarded molecule in the SMALL setting. We added them to revised paper, and included them in a form of Reaction SMILES in response to Reviewer **ptir** under *Pathways Examples* section.\n\nWe observe that, on average, cost-guided trajectories are ~10% cheaper. We selected a few example trajectory pairs and noticed that the cost reduction usually occurs by changing the order of the reactions: introducing expensive fragments later in the synthesis path, which decreases the product losses due to imperfect yields, and as a result increases the cost-efficiency. In **Example 1** (response to Reviewer **ptir** under *Pathways Examples***)**, SCENT (C) chooses the most expensive building block in the sequence (OB(O)c1ccc2[nH]ccc2c1) at the beginning of the synthesis, while SCENT (C) utilizes it in the second step, effectively reducing the synthesis cost by almost 28%. Cost-guided model also occasionally prefers cheaper fragments, e.g., *Brc1c[nH]cn1 instead of Ic1c[nH]cn1.* It is worth emphasizing that the fragments in the SMALL setting are already low cost, which is the reason why most gains are observed due to reaction order swapping.\n\nSimilarly, we compared the Dynamic Library, SCENT (C+D), to SCENT (C), and observed that on average the trajectories are 12% shorter and 6% cheaper. We selected few examples that visualized the compressed trajectories. We have found that the Dynamic Library enables the generation of convergent synthetic pathways (Example 2), which are responsible for the reduction of the synthesis cost. The application of the convergent synthetic strategy decreases the longest linear sequence (LLS) of steps, thereby increasing the overall yield. \n\nIn the MEDIUM settings, the SCENT variations diverged early on, so the set of shared molecules is very limited. Therefore, we matched high-rewarded molecules from SCENT (C) to closest high-rewarded molecule from SCENT (w/o C) with Tanimoto similarity > 0.6. We observed that, on average, the cost-guided trajectories are 20% cheaper. Thorough investigation of few selected trajectories pairs revealed that, differently from SMALL setting, in MEDIUM setting, the cost reduction comes mostly from the selection of cheaper fragments (Example 3). \n\n## No limitation section (and future work)\n\nWe apologize for the lack of limitation section, we are going to include the following limitation summary in the revised paper:\n\n“While our study demonstrates the effectiveness of SCENT in generating cost-efficient and synthesizable molecules, several limitations remain.\n\nFirst, the synthesis cost estimates are simplified and heuristic, omitting factors such as solvent usage, purification steps, hazardous reagents, reaction conditions, and scalability. Consequently, the current cost model should be regarded as a first-order approximation. More accurate estimation would require lab-specific calibration, including robust yield prediction models and vendor-specific building block pricing.\n\nSCENT’s performance varies across different biological targets, indicating that its effectiveness may depend on task-specific factors. Identifying and addressing these bottlenecks is an important direction for future work. Key areas of improvement include refining the exploitation behavior of the Dynamic Library, and enhancing the generalizability of the cost predictor model used in Synthesis Cost Guidance. Additionally, we do not assess performance as the number of reaction templates increases, an important scalability consideration, as this expansion significantly enlarges the action space and introduces additional learning challenges.\n\nOur framework emphasizes synthesizability and cost, but does not incorporate toxicity or broader ADMET properties, which are critical for downstream drug development. Finally, we acknowledge the absence of wet-lab validation, which remains the definitive benchmark for evaluating the practical utility of our approach. We leave this to future work.”\n\n## Unrealistic one-step fragments\n\nThe reaction Dynamic Library does take into account the cost of multi-step synthesis. Each intermediate molecule is assigned the lowest-cost synthesis path identified at the time it is added to the building block set. We acknowledge that this important detail was not clearly stated in the original manuscript, and we are grateful for the reviewer’s observation. We will clarify this point in the revised version of the paper."}, {"review_id": "spIQgdH30m", "reviewer": "Reviewer_QKfB", "summary": "This paper proposes SCENT (Scalable and Cost-Efficient de Novo Template-Based Molecular Generation), which combines several strategies with Reaction GFlowNet to generate diverse molecules while reducing synthesis cost. These strategies include Recursive Cost Guidance, which enables the model to focus on efficient sampling; Decomposability Guidance, which contributes to overall performance; an Exploitation Penalty; and a Dynamic Library of building blocks to increase mode coverage. In this way, the proposed method achieves state-of-the-art results among template-based approaches.", "questions": "**\"Regarding the effectiveness of the Dynamic Library (D):**\n\nThe results presented in Tables 1–4 indicate that the performance improvement from adding the Dynamic Library (i.e., comparing \"SCENT (C)\" to \"SCENT (C+D)\") varies significantly across different tasks and settings. For instance, while it yields notable gains in some configurations (e.g., GSK3β, LARGE setting in Table 3), its contribution appears more modest in others (e.g., sEH proxy under the MEDIUM setting in Tables 1 and 2, and JNK3 in Table 4).\n\nSince the Dynamic Library is intended to expand the explorable space by enabling richer, tree-structured synthesis pathways, one might anticipate more consistent performance gains.\n\n- Could the authors provide an analysis or hypothesis for this variability?\n- What factors might influence when the Dynamic Library is most effective versus when its impact is limited?\n\n**Regarding the performance of the Recursive Cost Guidance surrogate model:**\n\nThe effectiveness of the Recursive Cost Guidance framework critically depends on the predictive accuracy of the surrogate model, $\\hat{c}_B$. However, the paper does not report direct performance metrics for this model (e.g., validation loss or correlation with the target synthesis costs).\n\n- Could the authors provide more detail on the performance of this surrogate model?\n- Additionally, what are the potential consequences if the surrogate model makes inaccurate predictions? How robust is the overall method to such prediction errors?", "limitations": "As noted in the Weaknesses section, the authors do not address the scalability of their approach with respect to the number of reaction templates. This may constitute a potential limitation, particularly as the size of the action space increases in realistic scenarios.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\nThis paper introduces SCENT, a GFlowNet-based framework aimed at generating molecules that are both synthetically accessible and cost-efficient. Its key contributions include:\n\n- **Practical Relevance:** The study addresses an important challenge in drug discovery—producing molecules that are not only satisfied with targets but also straightforward and inexpensive to synthesize. By introducing an estimated synthesis cost into the generative process, the work moves closer to real-world applicability than approaches that focus solely on property optimization.\n- **Methodological Contributions (Originality & Quality):**\n    - The proposed **Recursive Cost Guidance** framework provides a systematic way to steer GFlowNet’s backward policy. Approximating an intractable recursive cost with a learned surrogate model ($\\hat{c}^B$) improves efficiency and not only directs generation toward lower-cost synthesis pathways but also increases mode coverage especially in settings with large building block libraries.\n    - To mitigate the over-exploitation observed with Recursive Cost Guidance, the authors introduce and show a simple yet effective exploitation-penalty term ($P_{E}$), as illustrated in Figure 5 and quantified in Table 1 for the medium- and large-building-block settings.\n    - The **Dynamic Library** (D) mechanism offers a form of augmenting in molecular generation by caching and reusing high-reward intermediates, enabling the construction of richer, tree-structured synthesis pathways shown in such as Table 1 and 3 of Large settings.\n- **Empirical Evaluation (Quality):**\n    - Experiments cover three library sizes (SMALL, MEDIUM, LARGE) and three molecular design tasks, providing evidence of robustness and scalability.\n    - SCENT outperforms several template-based GFlowNets (RGFN, SynFlowNet, RxnFlow) on metrics such as the number of high-reward compounds, modes, scaffolds discovered, and cost.\n    - Ablation studies isolate the effects of Cost Guidance, Dynamic Library, and Exploitation Penalty, clarifying each component’s contribution.\n\n### **Weaknesses**\n\nSeveral aspects remain insufficiently documented:\n\n- **Quantitative Computational Cost Analysis (Quality):** The paper does not report key compute metrics (e.g., GPU hours or other resource usage) for the small-, medium-, and large-setting configurations, nor does it compare them to other methods. The NeurIPS checklist notes that these data were not tracked, leaving efficiency claims unverified.\n- **Scalability with Reaction Templates (Quality):** While the study examines scaling with the number of *building blocks*, it does not evaluate how performance changes as the number of **reaction templates** grows, which would enlarge the action space and pose additional learning challenges.\n- **Lack of a Limitations Discussion (Clarity & Quality):** The paper omits a dedicated section on limitations. Issues such as the simplified cost model (which excludes solvents and purification) or potential failure modes of the guidance model are therefore not discussed, reducing transparency.\n- **Variable Performance (Quality):** In the JNK3 task (Table 4, SMALL and MEDIUM settings), the baseline RGFN discovers more diverse, high-reward modes and scaffolds than SCENT. Although the authors note that SCENT (C+P) generates the cheapest molecules, the paper provides limited analysis of why SCENT underperforms in this setting, which would help clarify the method’s boundaries.\n- **Hyper-parameter Settings (Minor):** Each component involves multiple hyper-parameters, yet the paper does not analyze how sensitive performance is to their values. Because an explore–exploit trade-off is plausible when increasing the number of building blocks and reaction templates, commenting on the method’s robustness under hyper-parameter variation would strengthen the work.", "was_revised": true, "final_justification": "Considering the overall evaluation and the discussion so far, I have decided to maintain my rating.", "rebuttal": "We truly value the reviewer’s detailed feedback and thoughtful recommendations. We have integrated all the suggestions, with the GPU resource metrics, cost model performance analysis, and the hyperparameter sensitivity study significantly enhancing the quality of our paper. We have addressed all the concerns raised and ask the reviewer to take these revisions into account during their evaluation.\n\n## Computational resources\n\nAs requested by the reviewers, we report GPU runtimes (in minutes) for all baselines on the sEH proxy, averaged over 3 random seeds using a V100 32GB GPU. All methods used a batch size of 64, except SynFlowNet, which required batch size 32 on the LARGE setting even after optimizing its policy code to fit within memory limits.\n\n| Model | SMALL | MEDIUM | LARGE |\n| --- | --- | --- | --- |\n| RGFN | 2882 ± 115 | 3892 ± 153 | 4455 ± 392 |\n| SynFlowNet | 200 ± 3 | 2350 ± 162 | 8416 ± 19 |\n| RxnFlow | 891 ± 4 | 3304 ± 44 | 3399 ± 50 |\n| SCENT (C+D+P) | 800 ± 15 | 2849 ± 144 | 3688 ± 317 |\n\nRGFN shares a part of the codebase with SCENT, allowing for direct comparison. We observe consistent improvements across all settings, particularly in the SMALL configuration. While SynFlowNet is faster on SMALL and MEDIUM, it struggles on LARGE, even with our memory optimizations. However, differences in codebases make direct comparisons less definitive.\n\nNote that our implementation is not optimized for runtime and could benefit from further improvements.\n\n## Scaling with number of reactions\n\nWe agree that scaling reaction-based generative models with the number of reaction templates is an important factor, omitted from the paper. However, curating a large, high-quality reaction set is a resource-intensive process that cannot be completed within the rebuttal period. Even with automated extraction (which would reduce reaction quality) the computational demands of evaluating all baselines exceed what is feasible in one week. We believe that our experiments already demonstrate the merits of our approach. While scaling with more templates could add value, it would not alter the key conclusions of our work - we propose this as a promising direction for future research.\n\n## Lack of limitation section\n\nWe apologize for the lack of limitation section, we are going to include the following limitation summary in the revised paper:\n\n“While our study demonstrates the effectiveness of SCENT in generating cost-efficient and synthesizable molecules, several limitations remain.\n\nFirst, the synthesis cost estimates are simplified and heuristic, omitting factors such as solvent usage, purification steps, hazardous reagents, reaction conditions, and scalability. Consequently, the current cost model should be regarded as a first-order approximation. More accurate estimation would require lab-specific calibration, including robust yield prediction models and vendor-specific building block pricing.\n\nSCENT’s performance varies across different biological targets, indicating that its effectiveness may depend on task-specific factors. Identifying and addressing these bottlenecks is an important direction for future work. Key areas of improvement include refining the exploitation behavior of the Dynamic Library, and enhancing the generalizability of the cost predictor model used in Synthesis Cost Guidance. Additionally, we do not assess performance as the number of reaction templates increases, an important scalability consideration, as this expansion significantly enlarges the action space and introduces additional learning challenges.\n\nOur framework emphasizes synthesizability and cost, but does not incorporate toxicity or broader ADMET properties, which are critical for downstream drug development. Finally, we acknowledge the absence of wet-lab validation, which remains the definitive benchmark for evaluating the practical utility of our approach. We leave this to future work.”\n\n## Why SCENT underperforms on JNK3?\n\nThank you for the thoughtful feedback. One notable characteristic of the JNK3 task is its sparse reward landscape - the number of discovered modes is significantly lower than in sEH and GSK3β. We hypothesize that in such settings, excessive exploitation driven by strong cost guidance may hinder mode discovery. Reducing the Synthesis Cost Guidance temperature (\\$\\alpha\\_S\\$) could help balance exploration and exploitation, potentially improving diversity at some cost trade-off. SCENT was not specifically tuned for JNK3, and experiments to test this hypothesis are ongoing - we will share the results as soon as they are available.\n\n## Hyperparameters\n\nTo assess SCENT (C+D+P) sensitivity to hyperparameters, we performed an ablation study on sEH MEDIUM, varying each hyperparameter around the paper’s chosen values (bolded in tables) over 3k iterations. Results indicate SCENT is generally robust, with no catastrophic performance drops. However, some hyperparameters notably affect outcomes; for example, simple tuning of $\\alpha_D$ can boost discovered modes by ~20%. This highlights potential for further improvements through targeted tuning, left for future work. Detailed results follow.\n\n| model | modes > 8.0 | scaff. > 8.0 | reward  | cost > 8.0 |\n| --- | --- | --- | --- | --- |\n| Synthesis Cost Guidance (temperature $\\alpha_S$) | - | - | - | - |\n| $\\alpha_S=2$ | 16036 ± 1153 | 22292 ± 1652 | 7.55 ± 0.05 | 1300 ± 82 |\n| **$\\alpha_S=5$** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n| $\\alpha_S=10$ | 13566 ± 4207 | 73916 ± 2516 | 7.87 ± 0.04 | 898 ± 42 |\n| Decomposability Guidance (temperature  $\\alpha_D$) | - | - | - | - |\n|  $\\alpha_D=2$ | 20023 ± 1484 | 45483 ± 2490 | 7.74 ± 0.04 | 1137 ± 130 |\n|  $**\\alpha_D=5$** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n|  $\\alpha_D=10$ | 20444 ± 2055 | 44938 ± 3111 | 7.74 ± 0.08 | 1141 ± 80 |\n| Dynamic Library (number of added molecules) | - | - | - | - |\n| L=200 | 19700 ± 1919 | 49119 ± 6425 | 7.72 ± 0.05 | 1087 ± 183 |\n| **L=400** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n| L=800 | 18373 ± 987 | 48016 ± 4302 | 7.71 ± 0.08 | 1060 ± 66 |\n| Dynamic Library (frequency of updates) | - | - | - | - |\n| T=500 | 15670 ± 2482 | 39701 ± 3676 | 7.66 ± 0.12 | 1130 ± 106 |\n| **T=1000** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n| T=2000 | 16148 ± 1753 | 39099 ± 3341 | 7.66 ± 0.08 | 1141 ± 82 |\n| Exploitation Penalty (initial $\\gamma_0$) | - | - | - | - |\n| $\\gamma_0=0.5$ | 20690 ± 1512 | 47220 ± 3676 | 7.69 ± 0.06 | 1154 ± 48 |\n| **$\\gamma_0=1.0$** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n| $\\gamma_0=2.0$ | 20655 ± 1514 | 45990 ± 1593 | 7.75 ± 0.04 | 1111 ± 77 |\n| Exploitation Penalty ($\\Delta \\gamma$) | - | - | - | - |\n| $\\Delta \\gamma=0.0$ | 20025 ± 762 | 43432 ± 796 | 7.75 ± 0.02 | 1222 ± 65 |\n| **$\\Delta \\gamma=0.2$** | 16373 ± 1827 | 39721 ± 3574 | 7.61 ± 0.03 | 1163 ± 81 |\n| $\\Delta \\gamma=0.4$ | 17189 ± 2234 | 42718 ± 2799 | 7.69 ± 0.04 | 1108 ± 165 |\n\n## Why Dynamic Library underperforms in some settings?\n\nWhile it is true that the dynamic library sometimes shows lower mode discovery in the online setting, it consistently yields higher-reward samples on average during inference. This behavior arises because dynamic library updates can occasionally favor fragments that bias exploration toward a narrower region of the search space. While this region may be high-reward, this comes at the cost of reduced diversity, which explains the lower mode discovery numbers highlighted in the review.\n\nThis trade-off suggests that the main limiting factor is the fragment selection criterion. Carefully designing this criterion to maintain diversity while still encouraging promising fragments is key to improving exploration and fully realizing the benefits of the dynamic library.\n\n## Performance of cost models\n\nThank you for the helpful suggestion regarding the cost prediction model. To better understand its performance, we trained it separately on a subset of molecules from SCENT trajectories in the SMALL (C) setting. For validation, we used a held-out set from the same setting and computed ground-truth costs using the full recurrence from Equation 7, whereas the training set relied on approximate estimates to simulate the online learning scenario.\n\nWe observed steady improvement during training, with validation R² peaking at 0.77 before overfitting set in, eventually declining to ~0.6, while training R² continued to rise to 1.0. While we’re unable to include plots at this rebuttal stage, we’ve added them to the revised version of the paper.\n\nIt’s worth noting that in the online setting, the model is trained on trajectories sampled by SCENT itself, helping it learn to identify low-cost backward trajectories. However, during inference, SCENT only uses forward sampling and does not rely on the cost predictor. Thus, while the model’s validation performance may influence trajectory selection during training, it does not affect SCENT’s inference procedure directly.\n\n## Consequences of inaccurate prediction model\n\nThe consequences of inaccurate cost predictions are assessed in Appendix G.2.2. We compared our model with a low-quality cost estimator that outputs a constant value for every input molecule, where this constant is the mean cost of all the building blocks in the given setting. It is important to note that we only modify the recursive component of the $C^S$ function from Equation (5), keeping the fragment costs unchanged. As shown in Table 8, this constant estimator can reduce costs and boost SCENT’s performance in MEDIUM and LARGE settings, but the machine learning-based cost model used in the main paper delivers far greater improvements, especially in cost reduction. This confirms that cost prediction quality is crucial for SCENT’s"}, {"review_id": "uzNgPQfjMs", "reviewer": "Reviewer_ptir", "summary": "In the manuscript, the authors present SCENT, an approach to template-based molecule generation. SCENT aims to minimize synthesis cost and increase molecule diversity. The authors propose recursive cost guidance to approximate synthesis cost, and design a dynamic library mechanism to reuse the intermediates with high reward. The evaluation results suggest the advantages of SCENT over the existing approaches.", "questions": "I strongly recommend the authors to show some case studies, including the yielded molecules, the reactions that yield them, together with synthesis costs. The ground-truth synthesis costs are also expected to judge the performance of the designed Recursive Cost Guidance module.", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength: \nSCENT estimates synthesis cost using a machine learning module, thereby enabling it to identify reaction pathways with low cost. \n\nWeakness: \nWhat is the ground-truth synthesis cost of a molecule? An example is expected to state the performance of the prediction of the synthesis cost. It is also much easier to distinguish the advantages of the presented approach if wet-lab experimental results are available.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their feedback. We have discussed all the issues raised and incorporated the suggestion to include case studies on generated synthesis trajectories which strengthen our paper. Given these revisions, we kindly hope the reviewer will consider updating their score.\n\n## Ground-truth synthesis cost of a molecule\n\nAs SCENT is designed for *de novo* molecular discovery, most of its outputs are novel and have not been synthesized before. Consequently, empirical synthesis costs are not available. The only definitive way to validate these costs would be through wet-lab experiments, which we agree would be valuable, but fall outside the scope of this work.\n\n## Wet-lab experiments\n\nWe appreciate the reviewer’s concern regarding the absence of wet-lab validation, which we fully acknowledge would significantly strengthen the benchmark. However, we believe this falls outside the typical scope of a machine learning conference. Our goal is to introduce and evaluate SCENT as a computational framework; while experimental validation is important, we argue it is not essential at this stage for demonstrating the method’s value in algorithmic and modeling terms. We see wet-lab validation as an important next step and a promising direction for future collaboration with experimental groups.\n\n## Case studies\n\nWe appreciate the reviewer’s suggestion and have conducted a detailed case study analyzing synthesis trajectories proposed by our model. While we are unable to include any images at this rebuttal stage, specific examples, including the generated molecules, the reactions used to construct them, and the associated synthesis costs, are provided in the next *Pathway Examples* section. The detailed analysis of the pathways can be found in our response to Reviewer **9Lb3**, under the section *Pathways Analysis*. It demonstrates that the Recursive Cost Guidance module effectively reduces synthesis cost by deferring the use of expensive fragments until later stages in the trajectory and favoring cheaper fragment’s alternatives when available. We will include the entire analysis, along with visualizations in the revised paper.\n\n## Pathways Examples\nThe extensive analysis of those synthesis pathways can be found in response to Reviewer **9Lb3**, under the section *Pathways Analysis*.\n\n### **Example 1, SMALL settings.**\n\n-------------------\n\n**SCENT (w/o C)**\n\nStep 1:\n\nOB(c(cc1)cc2c1[nH]cc2)O.Nc3c(O)cc(Br)cc3>>Nc4c(O)cc(c(cc5)cc6c5[nH]cc6)cc4\n\nReactant_1 cost: 12.56\n\nReactant_2 cost: 0.43\n\nReaction Yield: 75%\n\nProduct cost: 17.32\n\nStep 2:\n\nNc1c(O)cc(c(cc2)cc3c2[nH]cc3)cc1.O=C(C4CNC4)O>>c5(cc(c(cc6)cc7c6nc(C8CNC8)o7)cc9)c9[nH]cc5\n\nReactant_1 cost: 17.32\n\nReactant_2 cost: 0.19\n\nReaction Yield: 70%\n\nProduct cost: 25.01\n\nStep 3:\n\nClCC1CC1.c2(cc(c(cc3)cc4c3nc(C5CNC5)o4)cc6)c6[nH]cc2>>c78n(CC9CC9)ccc7cc(c(cc%10)cc%11c%10nc(C%12CNC%12)o%11)cc8\n\nReactant_1 cost: 0.33\n\nReactant_2 cost: 25.01\n\nReaction Yield: 75%\n\nProduct cost: 33.80\n\nStep 4:\n\nO=C(C1CNC1)O.c23n(CC4CC4)ccc2cc(c(cc5)cc6c5nc(C7CNC7)o6)cc3>>O=C(C8CCC8)N(C9)CC9c%10oc%11cc(c%12ccc%13n(CC%14CC%14)ccc%13c%12)ccc%11n%10\n\nReactant_1 cost: 0.19\n\nReactant_2 cost: 33.80\n\nReaction Yield: 75%\n\n**Product cost: 45.32**\n\n-------------------\n\n**SCENT (C)**\n\nStep 1:\n\nO=C(C1CNC1)O.Nc2c(O)cc(Br)cc2>>Brc(cc3)cc4c3nc(C5CNC5)o4\n\nReactant_1 cost: 0.19\n\nReactant_2 cost: 0.43\n\nReaction Yield: 70%\n\nProduct cost: 0.89\n\nStep 2:\n\nBrc(cc1)cc2c1nc(C3CNC3)o2.OB(c(cc4)cc5c4[nH]cc5)O>>c6(cc(c(cc7)cc8c7nc(C9CNC9)o8)cc%10)c%10[nH]cc6\n\nReactant_1 cost: 0.89\n\nReactant_2 cost: 12.56\n\nReaction Yield: 75%\n\nProduct cost: 17.93\n\nStep 3:\n\nClCC1CC1.c2(cc(c(cc3)cc4c3nc(C5CNC5)o4)cc6)c6[nH]cc2>>c78n(CC9CC9)ccc7cc(c(cc%10)cc%11c%10nc(C%12CNC%12)o%11)cc8\n\nReactant_1 cost: 0.33\n\nReactant_2 cost: 17.93\n\nReaction Yield: 75%\n\nProduct cost: 24.35\n\nStep 4:\n\nO=C(C1CNC1)O.c23n(CC4CC4)ccc2cc(c(cc5)cc6c5nc(C7CNC7)o6)cc3>>O=C(C8CCC8)N(C9)CC9c%10oc%11cc(c%12ccc%13n(CC%14CC%14)ccc%13c%12)ccc%11n%10\n\nReactant_1 cost: 0.19\n\nReactant_2 cost: 24.35\n\nReaction Yield: 75%\n\n**Product cost: 32.72**\n\n-------------------\n\n**SCENT (C+D)**\n\nStep 1:\n\nReactant_2 (c2(cc(c(cc3)cc4c3nc(C5CNC5)o4)cc6)c6[nH]cc2) was taken from the Dynamic Library\n\nClCC1CC1.c2(cc(c(cc3)cc4c3nc(C5CNC5)o4)cc6)c6[nH]cc2>>c78n(CC9CC9)ccc7cc(c(cc%10)cc%11c%10nc(C%12CNC%12)o%11)cc8\n\nReactant_1 cost: 0.33\n\nReactant_2 cost: 17.93\n\nReaction Yield: 75%\n\nProduct cost: 24.35\n\nStep 2:\n\nO=C(C1CNC1)O.c23n(CC4CC4)ccc2cc(c(cc5)cc6c5nc(C7CNC7)o6)cc3>>O=C(C8CCC8)N(C9)CC9c%10oc%11cc(c%12ccc%13n(CC%14CC%14)ccc%13c%12)ccc%11n%10\n\nReactant_1 cost: 0.19\n\nReactant_2 cost: 24.35\n\nReaction Yield: 75%\n\n**Product cost: 32.72**\n\n-------------------\n\n### **Example 2, SMALL settings.**\n\n-------------------\n\n**SCENT (w/o C)**\n\nStep 1:\n\nO=C([C@H]1CCCNC1)O.O=C([C@H]2CCCN2)O>>O=C([C@H]3CCCN3C([C@H]4CCCNC4)=O)O\n\nReactant_1 cost: 19.37\n\nReactant_2 cost: 0.10\n\nReaction Yield: 75%\n\nProduct cost: 25.97\n\nStep 2:\n\nO=C([C@H]1CCCN1C([C@H]2CCCNC2)=O)O.NCc(c[nH]3)c4c3cccc4>>O=C([C@H]5CCCN5C([C@H]6CCCN(C(NCc(c[nH]7)c8c7cccc8)=O)C6)=O)O\n\nReactant_1 cost: 25.97\n\nReactant_2 cost: 16.81\n\nReaction Yield: 75%\n\nProduct cost: 57.04\n\nStep 3:\n\nO=C([C@H]1CCCN1C([C@H]2CCCN(C(NCc(c[nH]3)c4c3cccc4)=O)C2)=O)O.NC5CC5>>O=C([C@H]6CCCN6C([C@H]7CCCN(C(NCc(c[nH]8)c9c8cccc9)=O)C7)=O)NC%10CC%10\n\nReactant_1 cost: 57.04\n\nReactant_2 cost: 0.02\n\nReaction Yield: 75%\n\nProduct cost: 76.07\n\nStep 4:\n\nO=C(NC1CC1)[C@H]2CCCN2C([C@H]3CCCN(C3)C(NCc4c5ccccc5[nH]c4)=O)=O.ClCC6CC6>>O=C(NC7CC7)[C@H]8CCCN8C([C@H]9CCCN(C9)C(NCc%10c%11ccccc%11n(CC%12CC%12)c%10)=O)=O\n\nReactant_1 cost: 76.07\n\nReactant_2 cost: 0.33\n\nReaction Yield: 75%\n\n**Product cost: 101.87**\n\n-------------------\n\n**SCENT (C)**\n\nStep 1:\n\nNC1CC1.O=C([C@H]2CCCN2)O>>O=C([C@H]3CCCN3)NC4CC4\n\nReactant_1 cost: 0.02\n\nReactant_2 cost: 0.10\n\nReaction Yield: 75%\n\nProduct cost: 0.16\n\nStep 2:\n\nO=C([C@H]1CCCN1)NC2CC2.O=C([C@H]3CCCNC3)O>>O=C([C@H]4CCCN4C([C@H]5CCCNC5)=O)NC6CC6\n\nReactant_1 cost: 0.16\n\nReactant_2 cost: 19.37\n\nReaction Yield: 75%\n\nProduct cost: 26.04\n\nStep 3:\n\nO=C([C@H]1CCCN1C([C@H]2CCCNC2)=O)NC3CC3.NCc(c[nH]4)c5c4cccc5>>O=C([C@H]6CCCN6C([C@H]7CCCN(C(NCc(c[nH]8)c9c8cccc9)=O)C7)=O)NC%10CC%10\n\nReactant_1 cost: 26.04\n\nReactant_2 cost: 16.81\n\nReaction Yield: 75%\n\nProduct cost: 57.14\n\nStep 4:\n\nO=C(NC1CC1)[C@H]2CCCN2C([C@H]3CCCN(C3)C(NCc4c5ccccc5[nH]c4)=O)=O.ClCC6CC6>>O=C(NC7CC7)[C@H]8CCCN8C([C@H]9CCCN(C9)C(NCc%10c%11ccccc%11n(CC%12CC%12)c%10)=O)=O\n\nReactant_1 cost: 57.14\n\nReactant_2 cost: 0.33\n\nReaction Yield: 75%\n\n**Product cost: 76.63**\n\n-------------------\n\n**SCENT (C+D)**\n\nStep 1:\n\nNC1CC1.O=C([C@H]2CCCN2)O>>O=C([C@H]3CCCN3)NC4CC4\n\nReactant_1 cost: 0.02\n\nReactant_2 cost: 0.10\n\nReaction Yield: 75%\n\nProduct cost: 0.16\n\nStep 2:\n\nO=C([C@H]1CCCN1)NC2CC2.O=C([C@H]3CCCNC3)O>>O=C([C@H]4CCCN4C([C@H]5CCCNC5)=O)NC6CC6\n\nReactant_1 cost: 0.16\n\nReactant_2 cost: 19.37\n\nReaction Yield: 75%\n\nProduct cost: 26.04\n\nStep 3:\n\nReactant_2 (NCc1c2ccccc2n(CC3CC3)c1) was taken from the Dynamic Library\n\nO=C([C@H]1CCCN1C([C@H]2CCCNC2)=O)NC3CC3.NCc4c5ccccc5n(CC6CC6)c4>>O=C(NC7CC7)[C@H]8CCCN8C([C@H]9CCCN(C9)C(NCc%10c%11ccccc%11n(CC%12CC%12)c%10)=O)=O\n\nReactant_1 cost: 26.04\n\nReactant_2 cost: 22.86\n\nReaction Yield: 75%\n\n**Product cost: 65.20**\n\n-------------------\n\n### **Example 3, MEDIUM settings.**\n\n-------------------\n\n**SCENT (w/o C)**\n\nStep 1:\n\nN#CCCNc1cc(OCCO2)c2cc1.Cn3cc(CNc4cc(C(O)=O)ccc4)cc3>>Cn5cc(CNc6cc(C(N(c7cc(OCCO8)c8cc7)CCC#N)=O)ccc6)cc5\n\nReactant_1 cost: 125.31\n\nReactant_2 cost: 279.35\n\nReaction Yield: 75%\n\n**Product cost: 539.55**\n\n-------------------\n\n**SCENT (C)**\n\nStep 1:\n\nN#CCCNc1cc(OCCO2)c2cc1.Nc3cc(c4cc(C(O)=O)ccc4)ccc3>>N#CCCN(C(c5cc(c6cc(N)ccc6)ccc5)=O)c7cc(OCCO8)c8cc7\n\nReactant_1 cost: 125.31\n\nReactant_2 cost: 27.27\n\nReaction Yield: 75%\n\n**Product cost: 203.45**"}, {"review_id": "B9asfDx9Qp", "reviewer": "Reviewer_E4oU", "summary": "This paper presents SCENT, a template-based molecular generation framework using Generative Flow Networks. The authors propose three key components: Recursive Cost Guidance to estimate and minimize synthesis cost, an Exploitation Penalty to encourage exploration, and a Dynamic Library mechanism to improve synthesis diversity and scalability. SCENT is evaluated on multiple tasks and fragment library sizes, showing improvements over several baselines in terms of synthesis cost, diversity, and reward.", "questions": "1. The authors mention using stock prices and reaction yields to model synthesis cost. Could the authors clarify how these are obtained in non-SMALL settings (e.g., Enamine pricing and literature yield extrapolation)? And how robust are these estimates to inaccuracies or variability across different datasets or vendors?\n\n2. Has SCENT been tested on a set of molecules with known real-world synthesis routes? Incorporating validation via retrosynthesis software (e.g., AiZynthFinder or ASKCOS) would strengthen the empirical claims.\n\n3. While ablations are performed (Section 4.2), could the authors provide a more fine-grained breakdown of individual contributions of Recursive Cost Guidance vs. Dynamic Library vs. Exploitation Penalty in all tasks (sEH, JNK3, GSK3β)?\n\n4. The performance of SCENT may depend on the hyperparameters of cost models (e.g., α, γ in penalty terms). Have the authors explored sensitivity analyses or automated tuning (e.g., Bayesian optimization)?\n\n5. The backward policy guidance is central to SCENT. Could the authors visualize or analyze specific trajectories to illustrate how cost guidance steers generation differently from baselines?", "limitations": "The authors discuss some limitations, such as the approximations in cost modeling and the assumption of reaction availability. However, more explicit acknowledgment of the risks of using heuristic or curated data, and the limited external validation, would strengthen this section. Overall, the paper shows strong potential, but a few methodological clarifications and validations would make it more convincing.", "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\n1. The Recursive Cost Guidance framework elegantly integrates cost estimation into the backward policy, offering a principled way to guide synthesis toward viable and low-cost molecules.\n\n2. The authors conduct extensive comparisons across multiple baselines (RGFN, SynFlowNet, RxnFlow) and settings (SMALL, MEDIUM, LARGE).\n\n3. SCENT consistently achieves better synthesis cost, reward, and diversity metrics, showing both practical and scientific utility.\n\n4. The use of a Dynamic Library shows notable gains in large-scale fragment settings, addressing a major bottleneck in template-based approaches.\n\n**Weaknesses**\n\nHowever, some aspects of the method remain unclear. The estimation of synthesis cost and reaction yield is somewhat opaque and heavily relies on heuristics or external databases, which may limit reproducibility and generalizability. Additionally, the effectiveness of the Dynamic Library and cost models is not fully validated outside of synthetic benchmarks.\n\n1. Some aspects of the cost prediction model (e.g., how exact yields and costs are estimated or normalized) are deferred to appendices and lack clarity in the main text.\n\n2. The reaction yield estimation and building block cost model may not generalize well across domains, as the current implementation is based on vendor-specific and literature-curated data.", "was_revised": true, "final_justification": "Given the overall quality of the work and the improvements made in response to the review, I will maintain my positive rating.", "rebuttal": "Thank you for your thoughtful feedback. We have carefully addressed all the concerns raised and have incorporated your suggestion regarding a more fine-grained study of component contributions. The corresponding ablation experiments are underway, and we will include their results in the revised camera-ready version of the paper. We hope these additions may help to further clarify our approach and underscore our commitment to a thorough evaluation. We would greatly appreciate your kind consideration of these improvements when reassessing the paper.\n\n## Yields and stock prices for MEDIUM and LARGE\n\nFor the **MEDIUM** and **LARGE** settings, stock prices were obtained by automatically scraping Enamine’s publicly available catalog. These prices represent a static snapshot at the time of data collection and do not reflect temporal fluctuations or pricing differences across vendors. Yield estimates are derived from literature-based averages, which provide reasonable robustness across diverse reaction classes. However, as discussed in the *Cost Estimates Generalization* section, these estimates may lack precision in the context of specific experimental setups or lab conditions.\n\n## Validation via retrosynthesis software\n\nSCENT is a de novo generative model that rarely reproduces molecules previously tested in the lab—typically, only ~1 in 150,000 generated compounds appears in ChEMBL. Thus, validating SCENT using retrosynthesis tools better reflects its intended use case than somehow testing it on a set of molecules with known synthesis routes. As shown in Figure 7, SCENT achieves a higher AiZynthFinder success rate than RGFN (likely due to its preference for shorter synthetic trajectories) and outperforms the non-template-based FGFN+SA, even though FGFN+SA is explicitly optimized for synthetic accessibility.\n\n## Fine-grained components’ contributions\n\nWe appreciate the suggestion and agree that a more fine-grained ablation would provide valuable insights. In response, we initiated experiments with SCENT (w/o C + P) and SCENT (w/o C + D) to better isolate the individual contributions of the Dynamic Library and the Exploitation Penalty across all tasks and settings.\n\nUnfortunately, these experiments are computationally intensive and have been delayed due to limited server availability, particularly during the rebuttal period, when compute resources are in high demand. While we are aiming to complete these runs before the end of the discussion phase on August 6th, we acknowledge that there is a chance they may not finish in time.\n\nRegardless, we are committed to including the full results in the camera-ready version of the paper and will notify the reviewers as soon as the results are available.\n\n## Cost Guidance hyperparameters\n\nIn the original paper, we have not explored the sensitivity, neither the automated tuning (we performed semi-manual small grid searches). Following the suggestion, we performed a detailed sensitivity analysis for each component (including temperature α for costs models, and γ in Exploitation Penalty). The results can be found in the response to Reviewer QKfB (Section *Hyperparameters*). Our results indicate that SCENT is generally resilient to variations in hyperparameters - its overall performance remains stable under a wide range of settings. That said, some hyperparameters have a notable impact on specific aspects of behavior; for example, changing $\\alpha_D$ temperature for Decomposability Guidance can lead to a ~20% increase in the number of discovered modes. This highlights the potential for further improvements through systematic hyperparameter optimization using Bayesian optimization, which we leave as future work.\n\n## Trajectory analysis\n\nAs suggested by the reviewer, we conducted a detailed analysis of the generated synthesis trajectories and found that the Synthesis Cost Guidance module influences generation in two key ways: (1) it defers the use of expensive fragments to later stages in the trajectory, effectively reducing the quantity of such fragments needed to produce the same amount of final product; and (2) it favors cheaper fragment alternatives when available. This behavior aligns with the module’s objective of minimizing overall synthesis cost. A thorough analysis, along with concrete examples, is included in our response to Reviewer **9Lb3** (see *Pathways Analysis* section). We are unable to include figures or PDFs during the rebuttal, but we will incorporate the corresponding visualizations in the revised version of the paper.\n\n## Cost estimates generalization\n\nWe agree that the yield estimation and cost model may not generalize well across different labs. It is, however, a consequence of the inherent chemical synthesis variability and the lab-specific factors (e.g. contracted vendor) being inherently hard to account for. To our knowledge, there is no universally applicable yield estimation method [1], neither a gold-standard scheme of pricing the building blocks. \n\nOur framework is as general as it can be, in the sense that it can accommodate any cost estimation method, including those that are accurate yet very lab-specific. \n\nMoreover, in a study requested by Reviewer 9Lb3 (see *Warm-start study* section), we demonstrate that SCENT’s cost prediction module can effectively adapt to random perturbations in pricing during training. When price changes occur, whether due to market fluctuations or vendor contract updates, they can be directly incorporated into the model. SCENT is able to adapt to these changes within approximately 1,000 training iterations, eliminating the need for retraining from scratch each time prices are updated. This warm-start capability significantly improves the model’s practicality in dynamic pricing environments.\n\n[1] “When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges”\n\n## Costs and yield deferred to appendices\n\nThank you for the comment, we considered this and would like to argue that the main focus of the paper is the generative framework, and the cost prediction model, while important for the framework to work, is in the end secondary. As such, we’d argue that keeping the details of this model in the appendix is appropriate. However, if the Reviewer feels strongly that shouldn’t be the case, we can incorporate additional details in the main body of the paper.\n\n## Limitation section\n\nWe apologize for the lack of limitation section, we are going to include the following limitation summary in the revised paper:\n\n“While our study demonstrates the effectiveness of SCENT in generating cost-efficient and synthesizable molecules, several limitations remain.\n\nFirst, the synthesis cost estimates are simplified and heuristic, omitting factors such as solvent usage, purification steps, hazardous reagents, reaction conditions, and scalability. Consequently, the current cost model should be regarded as a first-order approximation. More accurate estimation would require lab-specific calibration, including robust yield prediction models and vendor-specific building block pricing.\n\nSCENT’s performance varies across different biological targets, indicating that its effectiveness may depend on task-specific factors. Identifying and addressing these bottlenecks is an important direction for future work. Key areas of improvement include refining the exploitation behavior of the Dynamic Library, and enhancing the generalizability of the cost predictor model used in Synthesis Cost Guidance. Additionally, we do not assess performance as the number of reaction templates increases, an important scalability consideration, as this expansion significantly enlarges the action space and introduces additional learning challenges.\n\nOur framework emphasizes synthesizability and cost, but does not incorporate toxicity or broader ADMET properties, which are critical for downstream drug development. Finally, we acknowledge the absence of wet-lab validation, which remains the definitive benchmark for evaluating the practical utility of our approach. We leave this to future work.”"}, {"review_id": "WiH6zW1Bqk", "reviewer": "Reviewer_C46i", "summary": "The paper introduces SCENT, a novel framework for template-based molecular generation using GFlowNets. SCENT addresses key challenges in reaction-based GFlowNets by incorporating Recursive Cost Guidance, which leverages ML models to guide the generation process toward lower synthesis cost and valid backward transitions. The method further introduces an Exploitation Penalty to promote exploration and a Dynamic Library mechanism that reuses promising intermediate molecules, enabling full-tree synthesis and broader mode coverage.", "questions": "1. Could the authors provide a comparison of the performance of SCENT under the same training conditions with random cropping of the number of available fragments? This question follows from the statement “Previous work shows that reducing library size via random cropping can help, but the effect is modest compared to our gains” (L287-288).\n2. In Figure 6b, the number of modes for SCENT (C) and SCENT (C+P) seems to narrow at M=128k compared to M=64k. Could the authors elaborate on why this narrowing occurs despite having more building blocks?\n3. Could the authors provide a brief characterization of datasets (SMALL, MEDIUM, LARGE) beyond just size? For example, are there differences in molecular complexity, diversity, or chemical space coverage between the curated SMALL set and the larger Enamine sets? What supports the claim of \"broad coverage of molecular space\" (L587)?", "limitations": "The authors point out some limitations of the model in the text of the article, but it would be more useful to form a separate section in which the following points can be discussed:\n1. The paper focuses on synthesizability and cost, which are crucial, but does not include any assessment of the toxicity of the generated molecules. While outside the direct scope of the method, it's a critical aspect for drug discovery.\n2. The performance of SCENT (and baselines) shows variability across different tasks (sEH, GSK3β, JNK3). This suggests that the inherent suitability of the method might depend on the specific target, which could be a limitation in real-world applications.\n3. While the paper addresses synthesis cost, real-world synthesis complexity involves many factors beyond just building block cost and reaction yield L576-578 (e.g., purification challenges, hazardous reagents, reaction conditions, scalability). The current cost model is a first-order approximation (L151-155), and acknowledging these additional complexities would provide a more complete picture.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The proposed components (Recursive Cost Guidance, Exploitation Penalty, Dynamic Library) are clearly articulated and logically integrated into the GFlowNet framework, demonstrating a cohesive design.\n2. The paper provides comprehensive ablation studies that effectively demonstrate the individual contributions and synergistic effects of each proposed component, supporting the design choices.\n3. SCENT is rigorously compared against a strong set of relevant baselines (RGFN, SynFlowNet, RxnFlow, REINVENT, Graph GA) across multiple tasks and library sizes, providing convincing evidence of its superior performance.\n4. By focusing on synthesizability and cost-efficiency, SCENT directly addresses practical challenges in drug discovery, making generated molecules more useful for experimental use.\n\n**Weaknesses:**\n1. The paper lacks specific estimates of computational resources (e.g., training time on a particular GPU, memory usage) required for training SCENT, especially in comparison to baselines like RGFN. This information is crucial for reproducibility and practical adoption.\n2. While the sEH proxy task is mentioned, a brief explanation of what it measures, how its reward is calculated, and what a high score signifies would greatly enhance accessibility for readers less familiar with specific drug discovery benchmarks.\n3. The paper describes the architecture and training of the Synthesis Cost Model and Decomposability Model but does not provide performance metrics (e.g., R2 score for regression, accuracy/F1 score for classification) for these auxiliary models.\n4. The paper mentions Tanimoto similarity for the selection of molecules. However, it now seems more appropriate to use more modern metrics (e.g., MCES [1])  for assessing molecular similarity, which mitigate some of the limitations of Tanimoto similarity.\n\n[1] Kretschmer, F., Seipp, J., Ludwig, M., Klau, G. W., & Böcker, S. (2025). Coverage bias in small molecule machine learning. Nature Communications, 16(1), 554.\n\n**Minor comments:**\n1. A single, comprehensive diagram illustrating the entire SCENT pipeline, showing how the forward policy, backward policy, cost guidance, exploitation penalty, and dynamic library interact, would significantly improve clarity and understanding. Figure 1 is adequate but focuses on individual components rather than the full system flow.\n2. The paper sometimes uses \"exploration\" and \"exploitation\" in a way that could be slightly confusing (e.g., L309, L676 - “Exploration Penalty”, L10 and others - “Exploitation Penalty”).\n3. The labels \"w/\" in Figure 10 are ambiguous as it is stated in text that “the top n% of the most expensive molecules being removed.”", "was_revised": true, "final_justification": "The authors have addressed my concerns in their rebuttal, including clarifications on computational resources, cost model evaluation, dataset characterization, and limitations. The new details strengthen the paper’s clarity and reproducibility. While some minor issues remain, these do not substantially diminish from the contribution.", "rebuttal": "Thank you very much for your thoughtful and constructive review. We’ve carefully addressed all the issues you raised and have implemented your suggestions related to computational resource metrics and cost model evaluation. In particular, the evaluation of the Synthesis Cost Guidance is now complete, and we are actively working on the analysis of the Decomposability Model. \n\nWe believe these enhancements have significantly strengthened the paper and trust that the revisions will provide additional clarity and depth to our contributions.\n\n## Experiments with random cropping\n\nAs shown in Figure 6, reducing the fragment library size in SCENT (w/o C) generally leads to decreased or stagnant performance. Since the smaller libraries are strict and random subsets of the larger ones, this reduction effectively “crops” the available fragments. By contrast, adding cost biasing (C) and exploitative penalty (P) yields significant performance gains.\n\nFrom these results, we conclude that expanding the fragment library is more beneficial than cropping it, particularly when combined with cost biasing and exploitative penalty. The largest improvements are observed for SCENT (C+P), which underscores the importance of maintaining a rich and diverse fragment library.\n\n## Narrowed number of modes in 128k\n\nThank you for the insightful question. As the number of building blocks increases, the state space grows exponentially, which makes learning more challenging under a fixed training iteration budget. This may explain why the number of modes narrows at M=128k, despite having more fragments available. This trend is consistent with prior observations in related work (e.g., RGFN). Notably, SCENT is able to maintain strong performance even at higher fragment counts, significantly extending the range over which improvements are observed compared to RGFN.\n\n## Building block libraries analysis\n\nThank you for your helpful question. To characterize the SMALL, MEDIUM, and LARGE building blocks libraries beyond their size, we analyzed molecular diversity, complexity, and key physicochemical properties relevant to drug discovery. Specifically, we computed:\n\n- **Diversity:** Number of unique molecular modes and scaffolds, using leader clustering (with a Tanimoto similarity threshold of 0.3) to define clusters.\n- **Complexity:** Metrics including molecular weight and rotatable bonds.\n- **Physicochemical features:** Properties such as logP, hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and topological polar surface area (TPSA), which influence solubility, permeability, and binding potential.\n\nThe summary statistics are as follows:\n\n| Property | SMALL | MEDIUM | LARGE | Interpretation |\n| --- | --- | --- | --- | --- |\n| Number of modes | 154 | 9,738 | 16,181 | Molecular diversity |\n| Number of scaffolds | 39 | 8,118 | 16,149 | Scaffold diversity (core chemotypes) |\n| Molecular Weight (Da) | 150 ± 44 | 212 ± 62 | 221 ± 68 | Molecular complexity |\n| Rotatable Bonds | 1.23 ± 0.98 | 2.09 ± 1.61 | 2.34 ± 1.69 | Molecular flexibility/complexity |\n| logP | 0.96 ± 1.25 | 1.71 ± 1.18 | 1.80 ± 1.22 | Lipophilicity / solubility balance |\n| HBD | 1.12 ± 1.06 | 1.06 ± 0.76 | 1.07 ± 0.77 | Hydrogen bond donors (binding potential) |\n| HBA | 1.97 ± 0.84 | 2.77 ± 1.23 | 2.89 ± 1.31 | Hydrogen bond acceptors |\n| TPSA (Å²) | 41.0 ± 23.4 | 49.9 ± 22.9 | 51.0 ± 23.8 | Polar surface area (transport/permeability) |\n\nThese data show that the curated SMALL dataset represents a focused set of chemically simpler molecules with fewer scaffolds and lower molecular weight, while the MEDIUM and LARGE datasets encompass increasingly diverse chemical scaffolds and more complex molecules. The LARGE set in particular covers a broad chemical space, as indicated by the large number of unique scaffolds and modes and the wider range of physicochemical properties.\n\nThis comprehensive characterization supports our claim of “broad coverage of molecular space” by demonstrating substantial diversity in core scaffolds and molecular features critical to drug discovery across the datasets.\n\n## Computational resources\n\nAs requested by the reviewers, we report GPU runtimes (in minutes) for all baselines on the sEH proxy, averaged over 3 random seeds using a V100 32GB GPU. All methods used a batch size of 64, except SynFlowNet, which required batch size 32 on the LARGE setting even after optimizing its policy code to fit within memory limits.\n\n| Model | SMALL | MEDIUM | LARGE |\n| --- | --- | --- | --- |\n| RGFN | 2882 ± 115 | 3892 ± 153 | 4455 ± 392 |\n| SynFlowNet | 200 ± 3 | 2350 ± 162 | 8416 ± 19 |\n| RxnFlow | 891 ± 4 | 3304 ± 44 | 3399 ± 50 |\n| SCENT (C+D+P) | 800 ± 15 | 2849 ± 144 | 3688 ± 317 |\n\nRGFN shares a part of the codebase with SCENT, allowing for direct comparison. We observe consistent improvements across all settings, particularly in the SMALL configuration. While SynFlowNet is faster on SMALL and MEDIUM, it struggles on LARGE, even with our memory optimizations. However, differences in codebases make direct comparisons less definitive.\n\nNote that our implementation is not optimized for runtime and could benefit from further improvements.\n\n## Explain sEH\n\nFollowing the suggestion of the reviewer we added a brief explanation of sEH proxy to the revised paper: “The **sEH proxy** is a graph neural network  trained to predict the binding affinity of small molecules to **soluble epoxide hydrolase (sEH)**, using docking scores computed by AutoDock Vina as training labels. The higher the reward defined by sEH proxy, the more strongly a given molecule binds to sEH.”\n\n## Performance of cost models\n\nThank you for the valid suggestion. In response to Reviewer QKfB, we conducted an analysis of the cost model used in Synthesis Cost Guidance (see *Performance of Cost Models*). A similar analysis for the Decomposability Cost Guidance is currently in progress, and we will provide the results as soon as they become available.\n\n## MCES instead of Tanimoto\n\nThank you for drawing our attention to MCES. We agree that it can offer a more nuanced measure of molecular similarity compared to Tanimoto similarity. However, we opted for Tanimoto primarily because (1) it is widely used in related work, (2) it is significantly faster to compute.\n\n## Limitation section\n\nWe apologize for the lack of limitation section, we are going to include the following limitation summary in the revised paper:\n\n“While our study demonstrates the effectiveness of SCENT in generating cost-efficient and synthesizable molecules, several limitations remain.\n\nFirst, the synthesis cost estimates are simplified and heuristic, omitting factors such as solvent usage, purification steps, hazardous reagents, reaction conditions, and scalability. Consequently, the current cost model should be regarded as a first-order approximation. More accurate estimation would require lab-specific calibration, including robust yield prediction models and vendor-specific building block pricing.\n\nSCENT’s performance varies across different biological targets, indicating that its effectiveness may depend on task-specific factors. Identifying and addressing these bottlenecks is an important direction for future work. Key areas of improvement include refining the exploitation behavior of the Dynamic Library, and enhancing the generalizability of the cost predictor model used in Synthesis Cost Guidance. Additionally, we do not assess performance as the number of reaction templates increases, an important scalability consideration, as this expansion significantly enlarges the action space and introduces additional learning challenges.\n\nOur framework emphasizes synthesizability and cost, but does not incorporate toxicity or broader ADMET properties, which are critical for downstream drug development. Finally, we acknowledge the absence of wet-lab validation, which remains the definitive benchmark for evaluating the practical utility of our approach. We leave this to future work.”"}]

Author rebuttal:

Meta-review: This paper addresses the problem of molecular generation and proposes a new method based on the recently introduced GFlowNets, while also addressing their drawbacks, namely, generating diverse molecules while reducing synthesis cost.

The paper is overall well written and well organized. The proposed method has several strengths: it incorporates synthesis cost into the generation process, which is a practically relevant contribution; it leverages a dynamic library for more effective molecule generation; and it provides a thorough empirical evaluation.

The reviewers raised several concerns, such as the estimation of synthesis cost, evaluation of scalability, and the benefits of using the dynamic library. However, the authors responded well in their rebuttal, and all reviewers, except for one who remained unresponsive, expressed satisfaction and are positive about acceptance.

In my view, one weakness is that the proposed method is presented by describing each component in a straightforward manner. If Section 3 included a theoretical analysis or discussion of the properties of these individual components, the paper would be of even higher quality.

Overall, I believe this paper makes a strong contribution and I recommend its acceptance.
Decision: Accept (poster)