Title: Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: drug discovery

generative models

GFlowNets

synthesizability

molecular generation
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.”\n\nQUESTIONS:\n1. 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)?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strength: 
SCENT estimates synthesis cost using a machine learning module, thereby enabling it to identify reaction pathways with low cost. 

Weakness: 
What 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.

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
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-19

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### **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.\n\nQUESTIONS:\n**\"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?\n\nLIMITATIONS:\nAs 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n* 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-21"}