Title: Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Structure-based Drug Design

3D Molecule Generation

Generative Model
Abstract: Recent advances in Structure-based Drug Design (SBDD) have leveraged generative models for 3D molecular generation, predominantly evaluating model performance by binding affinity to target proteins. However, practical drug discovery necessitates high binding affinity along with synthetic feasibility and selectivity, critical properties that were largely neglected in previous evaluations. To address this gap, we identify fundamental limitations of conventional diffusion-based generative models in effectively guiding molecule generation toward these diverse pharmacological properties. We propose $\texttt{CByG}$, a novel framework extending Bayesian Flow Network into a gradient-based conditional generative model that robustly integrates property-specific guidance. Additionally, we introduce a comprehensive evaluation scheme incorporating practical benchmarks for binding affinity, synthetic feasibility, and selectivity, overcoming the limitations of conventional evaluation methods. Extensive experiments demonstrate that our proposed $\texttt{CByG}$, framework significantly outperforms baseline models across multiple essential evaluation criteria, highlighting its effectiveness and practicality for real-world drug discovery applications.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a generative framework for structure-based drug design that combines Bayesian Flow Networks with gradient guidance. It handles both the prediction of atomic types and 3D coordinates and enables property-driven molecule generation. It outperforms existing methods in binding affinity, synthetic feasibility, and selectivity, validated through diverse docking tools.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1)The integration of gradient-based guidance into Bayesian Flow Networks for 3D molecule generation is novel and conceptually sound as well as addresses fundamental limitations in classical diffusion-based models. \n\n2)The work includes multiple baselines and evaluation metrics (e.g., GNINA, SMINA, Vina, PoseBusters, AiZynthFinder), offering a comprehensive evaluation of the generative capabilities.\n\nWeakness:\n1)Each individual component is known separately. The novelty primarily lies in their theoretical integration and application context rather than entirely new algorithm. Specifically, the novelty lies more in their theoretical unification and application context than in introducing fundamentally new mechanisms.\n\n2)The paper mentions at the end of Section 5.2 and in Appendix D the use of a Bayesian Neural Network to quantify predictive uncertainty and incorporate it into the sampling process. This is a sophisticated design that potentially makes the guidance more robust. However, the description in the main text is too brief for readers to understand. I believe that the specifics of this mechanism's implementation (e.g., how does uncertainty modulate the guidance scale?) and its contribution to the final performance (e.g., an ablation study comparing it to deterministic guidance without a BNN) are critical details of the CByG framework. These deserve more space in the main text rather than being relegated entirely to the appendix.\n\n3)Theoretical derivations in Section 4, especially 4.2 and 4.3, lack rigor due to missing key assumptions. For instance, the use of Tweedie’s formula (Eq. 6) to reinterpret Bayesian updates as gradient-based guidance lacks justification—there’s no discussion of required conditions like distributional assumptions or continuity. In Eq. 5–8, the transition from Bayesian update to score-based form is asserted without formal derivation. Additionally, it’s unclear whether gradients are taken in data or parameter space, which weakens the theoretical clarity of the proposed method.\n\nQUESTIONS:\n1)The theoretical derivations in Section 4, especially the use of Tweedie’s formula and the transition from Bayesian updates to gradient-based forms, lack formal justification. Key assumptions—such as distributional properties and whether gradients are taken in data or parameter space—are not clearly stated. A more rigorous derivation with explicit assumptions would greatly strengthen the theoretical foundation and improve the credibility of the method.\n\n2)Could you provide a detailed ablation study focusing specifically on the key hyperparameters (e.g., gradient guidance strength, Bayesian neural network design choices)? Clarifying how sensitive your results are to these parameters would significantly enhance the robustness and credibility of your findings. Providing clear criteria or thresholds demonstrating stability or sensitivity would positively influence my evaluation.\n\n3)Could you provide an analysis of computational cost in different experimental settings to elaborate the efficiency of the method?\n\n4)Could you elaborate the treatment of categorical variables in Section 4.3 as discrete variables can be handled through gradient-based updates contradicts the fundamental nature of categorical distributions. The softmax operation in Equation (10) does not resolve the non-differentiability issue that the authors initially identified as a problem with diffusion models.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents CBYG, which extends Bayesian Flow Networks (BFNs) into a gradient-based conditional generative model that robustly integrates property-specific guidance. The authors also introduce new metrics to include synthetic feasibility and selectivity, and show clear performance gains.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n- This paper moves beyond mere binding affinity and incorporate synthetic feasibility (via AiZynthFinder) and selectivity into SBDD evaluation.\n- This paper reformulates Bayesian updates for both continuous and discrete variables into a gradient-based form via Tweedie's formula, which I think brings theoretical insight for Bayesian Flow Network (BFN), and the authors have empirically demonstrated its superiority over gradient guided diffusion frameworks.\n\nWeakness:\n- Clarity of derivations can be further improved. $\\mu_x$ and $x$ in Equation 6 is not defined, and it seems that the notation $x$ is abused for both the noised version and the ground truth. For Equation 7, I recommend the authors to split the first line and the second line because they are essentially coming from different derivations, and it's best not to confuse them. $\\delta$ in line 177 is not clearly presented, either. It's difficult to understand why it is a compact notation because it obviously introduced more notations than the right-hand-side. Overall I think section 4 should be explained more clearly to make this paper more accessible to broader audiences.\n- Lack of referencing a directly relevant work MolJO, which is also a gradient guided BFN method [1].\n- Derivation of Section 4.3 (BFN for Discrete Variables from Score Gradient) is confusing and seems incorrect. The score $\\nabla_{e^x}\\log p(e^x)$ in L196 and Eq. (10) is undefined, as L188 states that $e^x$ is a one-hot vector. Tweedie's formula does not hold for non-Gaussian variables.\n\nI will consider raising my score if my concerns are adequately addressed.\n\n[1] https://arxiv.org/abs/2411.13280\n\nQUESTIONS:\n1. I am curious about the benefit of BFN over diffusion. The authors claim in Appendix B.3 that BFN involves deterministic parameter update that remains strictly deterministic given y (containing the implicit stochasticity), and that the update rule can be directly interpreted through the lens of Kalman filtering for scalar Gaussian models. Can the authors elaborate on this point? What advantages does this bring to the training and sampling of BFN over diffusion / SDE? I know this may be hard, but can the authors provide a more quantitative analysis based on their theoretical construction, or just shed some light on it?\n2. Given the Tweedie's formula, it seems that we need to perform guidance over y for discrete data, as done in MolJO. Can the authors correct their derivation based on that observation or in some other way? How would that compare with guidance over $\\theta$ in the continuous case?\n3. How is AiZynthFinder's score correlated with real-world synthetic feasibility?\n4. How is PB-Valid calculated? The authors mention that they adopt 17 metrics, but PoseBusters test suite has more than 17 checks. Can the authors provide detailed validity ratio for each metric? Otherwise I remain doubtful of the reproducibility of PB-Valid results, as the PB-Valid of CBYG largely exceeds its backbone MolCRAFT (94.9% vs. 74.0%) without explicitly incorporating structural validity into its optimization objective, which seems unrealistic to me. Moreover, is RGA or TacoGFN generating molecules in the 3D space? If not, what's the point of calculating their structural validity?\n\nLIMITATIONS:\nThis paper misses at least the mentioning of the validation and credibility of AiZynthFinder benchmark, which will make it more self-contained, and better indicative of its contribution towards addressing the limitation of previous SA metrics. Besides, the authors mention that approximately half of the molecules generated by CBYG remain synthetically infeasible without further explanation.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors present a gradient-based representation for Bayesian Flow Networks suitable for diffusion-guided structure-based molecular generation. As their main contributions, the authors highlight specific challenges surrounding diffusion-guidance for SBDD applications (invalid intermediate samples, synthesizability, selectivity), propose a novel update for bayesian flows and apply this model to molecular generation tasks. The authors evaluate the proposed model against a number of SBDD baselines and show favourable performance across binding affinity and synthesizability evaluations.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\nOverall, the manuscript is of good quality and generally well written. In Section 2, the authors clearly identify a number of challenges motivating the presented work. The proposed perspective for the bayesian update is presented in sufficient detail and the evaluation procedure addresses important challenges of molecular generation, especially for struture-based continuous methods. Figures 2, 3, 4 effectively support the main text.\n\n## Weaknesses\n\n### Lack of justification and context for diffusion-based models for drug design\n\nOne of my main critiques is the absence of context to justify SBDD relying on diffusion models. In particular, the authors highlight in Section 2 a number of challenges relating to guidance, synthesizability and multi-parameter optimization which affect diffusion-based models for drug design applications. Some of these challenges are absent or considerably mitigated with discrete optimisation approaches such as Reactor [1], Synthemol [2], Synflownet [3] which can incorporate synthesizability constraints directly into the design of the MDP and use a reinforcement learning paradigm which more naturally lend itself to multi-objective optimization. This contrast is not being discussed either in the introduction, problem presentation or related work. I believe it is important to situate the reader with respect to the pros and cons of both family of approaches (structure-based continuous generators v.s. RL-based constrained generators).\n\n[1]: Horwood, J., & Noutahi, E. (2020). Molecular design in synthetically accessible chemical space via deep reinforcement learning. ACS omega, 5(51), 32984-32994.\n\n[2]: Swanson, K., Liu, G., Catacutan, D. B., Arnold, A., Zou, J., & Stokes, J. M. (2024). Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nature Machine Intelligence, 6(3), 338-353.\n\n[3]: Cretu, M., Harris, C., Igashov, I., Schneuing, A., Segler, M., Correia, B., ... & Liò, P. (2024). Synflownet: Design of diverse and novel molecules with synthesis constraints. arXiv preprint arXiv:2405.01155.\n\n### Other elements hindering clarity\n\n- I believe the Related Work should be presented in the main paper. It is reasonable to extend the related work section in appendix but in its current version the manuscript lacks context and references to previous work in the main text.\n- In Figure 1: the axes are not identified, not clear what we are looking at. The caption is not particularly helpful either in fully understanding what is being shown. This Figure needs to be clarified.\n- It is unclear from the main text which models are used for synthesizability and selectivity guidance for the experiments carried out in Sections 6.3 and 6.4.\n\n### Insufficiently supported claims\n\n- In the abstract, line 12, the claim that the limitations of previous evaluation methods have been \"overcomed\" is too strong. I would suggest tuning it down -- there certainly remains numerous limitations to the proposed evaluation paradigm.\n- On lines 93-94: \"However, high SA scores often do not guarantee practical synthetic routes, highlighting a critical gap in accurately evaluating real-world synthesizability.\" -> this observation has been frequently made and would benefit from pointers to the relevant litterature. Scrutiny into SA score is again overclaimed on lines 253-257: \"To thoroughly evaluate the effectiveness and practicality of our proposed framework, we define 4 key research questions essential to the domain of structure-based drug design: [...] 2) Is the widely-used Synthetic Accessibility (SA) score an absolute indicator for practical synthetic 257 feasibility?\". This question has been thoroughly studied and almost always answered negatively in previous work (for exemple but not limited to: [4, 5]). While it is relevant for the authors to highlight the limitations of the SA score, this phrasing makes it sound as if this investigation was an orginal contribution of the presented work. However, nothing forces the author to even reference that metric which is largely seen as outdated. It is indeed crucial to consider synthesizability challenges of SBDD applications. I would simply recommend to authors to cite the relevant and most up-to-date litterature for synthesizability scoring (AIzynthfinder, as the authors use in Section 6.3 is a reasonable choice). Finally, again on lines 306-307, the authors portray as \"novel\" an observation which has been repeatedly made in previous work: \"Interestingly, we observed no clear correlation between SA scores and actual retrosynthetic feasibility 307 (as measured by the Solved metric)\".\n\n[4]: Coley, C. W., Rogers, L., Green, W. H., & Jensen, K. F. (2018). SCScore: synthetic complexity learned from a reaction corpus. Journal of chemical information and modeling, 58(2), 252-261.\n\n[5]: Liu, C. H., Korablyov, M., Jastrzebski, S., Włodarczyk-Pruszynski, P., Bengio, Y., & Segler, M. (2022). RetroGNN: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software. Journal of Chemical Information and Modeling, 62(10), 2293-2300.\n\n### Typos and syntax\n\n- The hyperlinks seem to be missing (on citations, section numbers, etc.). I cannot click on them to jump to other sections.\n- In lines 69-86, I'd suggest using a different letter than $l$ for the labels/properties (ambiguous).\n- Figure 3 is not referenced anywhere in the text.\n\n## Conclusion\n\nI believe the presented work is of good quality, and of interest to the NeurIPS community, but that it could also be improved by better contextualizing this line of work (diffusion models for structure-based molecular generation) and tuning down some of its claims.\n\nQUESTIONS:\n1. AIzynthfinder is used to assess synthesizability in Section 6.3. Is a different model used for guidance?\n2. How distinct from the training set are the molecules generated by the model?\n3. While the authors present an improved diffusion-based method for synthesizability guidance, it is not clear how this synthesizability evaluation can be rooted in specific chemistry capabilities. The use of generic synthesizability scores would lead to compounds that are believed to be synthesizable \"in general\", or simply similar to known compounds, but specific chemistry routes still have to be designed by hands (or through retrosynthesis) for every generated compound, without the possibility of binding the generation process to a particular vendor's catalog. Given this limitation, it appears that the method is primarily aimed a low-throughput, custom chemistry stages rather than large-scale hit identification?\n\nLIMITATIONS:\nNot quite. I found little discussion on the limitations of this line of work in general (structure-based continuous molecular generators) and for the presented method in particular (except about one line in the conclusion).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors identify fundamental concerns with gradient-based guidance applied in sample space (e.g., when using diffusion models for structure-based drug discovery (SBDD)) and subsequently propose a Bayesian flow network for parameter space gradient-based guidance in SBDD. Empirical results for existing and new benchmarks are compelling and further suggest that future work (for all generative methods) is needed to improve the practical synthesizability of each method's generated molecules.\n\nSTRENGTHS AND WEAKNESSES:\n**Points of strength:**\n1. The authors identify and propose a solution to the challenges of sample space gradient-based guidance in SBDD (e.g., with diffusion models).\n2. The authors' proposed CByG network achieves strong empirical results for existing and new SBDD benchmarks.\n3. The authors' writing and methodological rationale are clear and convincing.\n\n**Points for improvement:**\n1. Some of the authors' claims may not be entirely accurate. For example, in Section 6.2, the authors state that they introduce PoseBusters docking scores [1] into SBDD evaluation criteria. However, previous works in SBDD with diffusion models have already done so, as shown in works such as [2] (and such baselines are missing from the results in Table 1).\n2. The authors should consider including a second benchmark dataset for SBDD sample scoring, such as Binding MOAD, PDBBind, or PLINDER. This would help to address a concern that CByG is simply overfitting to CrossDocked-like protein-ligand complexes.\n\n**References:**\n\n[1] Buttenschoen, M., Morris, G. M., & Deane, C. M. (2024). PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 15(9), 3130-3139.\n\n[2] Morehead, A., & Cheng, J. (2024). Geometry-complete diffusion for 3D molecule generation and optimization. Communications Chemistry, 7(1), 150.\n\nQUESTIONS:\nCould the authors discuss what (practical or empirical) disadvantages they have found when developing Bayesian flow networks for molecular data? Are they harder to train than diffusion models, for example?\n\nLIMITATIONS:\nThe authors currently only include one core SBDD benchmark dataset for general sample scoring (i.e., CrossDocked). Including another dataset for benchmarking would improve the rigor of the authors' experiments.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-08"}

comments: SUMMARY:
This paper addresses a practical and important challenge in structure-based drug design: generating 3D molecules that are not only high-affinity binders, but also selective and synthesizable. The authors propose CByG, a novel framework that integrates Bayesian Flow Networks with gradient-based guidance. Using Tweedie’s formula, they reinterpret Bayesian updates as gradient steps, allowing controllable generation in parameter space. The paper also presents a well-rounded evaluation framework. Results show CByG outperforms existing methods across key metrics.

STRENGTHS AND WEAKNESSES:
Strengths
• Clear problem statement and strong motivation
• Strong theoretical link between Bayesian updates and gradient guidance
• Addresses practical needs beyond binding affinity by incorporating synthesizability and selectivity into both modeling and evaluation
• Clear writing and well-organized structure
Weaknesses
• The discussion of synthesizability results is somewhat limited.
• The paper does not provide analysis of training or inference efficiency.

QUESTIONS:
1. The "Solved" rate in AiZynthFinder is below 50% despite high SA scores. Could the authors briefly discuss why this gap exists?
2. No runtime comparison is provided. Can the authors include training/inference time versus diffusion models?
3. Line 302 incorrectly refers to “Table 3” for AiZynthFinder results. Please correct.
4. Line 300 mentions six metrics, but only four are shown in Table 2. Please clarify.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-16