Title: Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
Venue: NeurIPS 2025
Accepted: yes
Keywords: Structure-based drug design

Molecule Generation

Generative models

Flow Matching

Computational Biology
Abstract: Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent generative models have shown great potential, they suffer from unstable probability dynamics and mismatch between generated molecule size and the protein pockets geometry, resulting in inconsistent quality and off-target effects. We propose PAFlow, a novel target-aware molecular generation model featuring prior interaction guidance and a learnable atom number predictor. PAFlow adopts the efficient flow matching framework to model the generation process and constructs a new form of conditional flow matching for discrete atom types. A protein–ligand interaction predictor is incorporated to guide the vector field toward higher-affinity regions during generation, while an atom number predictor based on protein pocket information is designed to better align generated molecule size with target geometry. Extensive experiments on the CrossDocked2020 benchmark show that PAFlow achieves a new state-of-the-art in binding affinity (up to -8.31 Avg. Vina Score), simultaneously maintains favorable molecular properties.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes PAFlow, a novel flow matching (FM)-based generative model for structure-based drug design (SBDD) that addresses key limitations of existing autoregressive and diffusion-based approaches, such as unnatural generation orders, unstable denoising trajectories, and reliance on predefined ligand priors for atom number prediction. PAFlow introduces a conditional flow matching (CFM) method for discrete atom types alongside variance-preserving (VP) paths for continuous coordinate generation, integrates a protein-ligand interaction predictor to enhance binding affinity, and employs a geometry-aware atom number predictor to eliminate dependency on reference ligands. Experiments on CrossDocked2020 demonstrate state-of-the-art performance, achieving an average Vina score of -8.31 (a 1.24 improvement over baselines) while maintaining favorable molecular properties, establishing FM as a robust alternative for 3D molecule generation in drug discovery.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n1. The proposed method demonstrates outstanding performance in terms of binding affinity scores on the CrossDocked2020 benchmark, surpassing existing baselines by a substantial margin.\n2. The flow-based generative framework employed in the model exhibits superior sampling efficiency compared to diffusion-based counterparts, as evidenced by the reported generation time.\n## Weaknesses:\n1. Although the model achieves high predicted Vina scores under classifier guidance, it is well-known that such guidance often leads to ill-formed molecular conformations, such as abnormal bond lengths, distorted bond angles, and overly twisted structures. The paper lacks essential evaluation metrics that are standard in recent works [1,2,3], including bond length/angle JSD, validity, ring size distribution, strain energy, and atom clash statistics.\n2. The authors claim that the learnable atom number predictor addresses the mismatch between ligand size and binding pocket geometry caused by prior-based methods. However, the experimental section does not include any results or ablation studies to validate this claim.\n3. Prior research [4] has established a strong positive correlation between ligand size (i.e., number of atoms) and binding affinity. It remains unclear whether the performance gain attributed to the proposed atom number predictor stems merely from generating larger molecules. The paper does not analyze whether the predicted atom counts differ significantly from traditional methods or how this affects binding affinity.\n4. The protein–ligand interaction predictor used for classifier guidance appears to be functionally equivalent to the affinity predictor introduced in TAGMol. Given this similarity, it is questionable to position this component as a major contribution of the paper.\n\nReferences:\n\n[1] Guan et al., DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. arXiv:2403.07902\n\n[2] Zheng et al., PoseCheck: Robust Pose Filtering for Diffusion-Based Molecular Docking. arXiv:2308.07413\n\n[3] Qu et al., MolCraft: Structure-based Drug Design in Continuous Parameter Space. arXiv:2404.12141\n\n[4] Wang et al., CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph. arXiv:2406.10840\n\nQUESTIONS:\nSee in weakness.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors aim to solve the problem of structure-based drug design. Upon this, the authors noticed limitations of existing generative methods, such as unstable probability dynamics and molecular size mismatches. To address such limitations, the authors introduce PAFlow, a novel target-aware molecular generation model designed for structure-based drug design. PAFlow addresses such limitations by leveraging the flow matching framework with a new conditional flow matching for discrete atom types, incorporating a protein-ligand interaction predictor for guiding generation towards high-affinity regions. Additionally, it integrates a learnable atom number predictor, relying solely on protein pocket information, which significantly improves the alignment of generated molecule size with target geometry, achieving state-of-the-art binding affinity results while maintaining favorable molecular properties.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- Extensive evaluation and good performance: The paper contains a comprehensive comparison with a number of baselines on a large-scale benchmark with comprehensive metrics, and necessary ablation studies of the proposed method, which helps to understand the empirical robustness of the proposed method.\n- Overall decent recipe to solve SBDD: The proposed method appears to be a well-developed methodology that efficiently utilizes prior work formulations (probability density path formulation, prior guidance) to solve current SBDD problems, while adding unique methods (proposition, learnable atom predictor) to overcome the limitations of prior work.\n- Efficient improvement over previous methods: The introduction of an atom predictor effectively resolves the issue that non-autoregressive previous works for SBDD problems required reference information for generated ligands. In addition, the sampling efficiency over baseline is comparable to the most efficient baselines.\n\n## Weaknesses\n\n- Incremental methodological contribution: The method appears to be largely similar in framework to [1] in terms of using flow matching and prior guidance, and to [2] in terms of probability density path formulation, suggesting that the novelty may be limited to incremental improvements.\n- Lack of formulation justification: In Equation (4), the probability density is defined as the product of probability distributions of atom type and atom coordinate. Given that these two features have strong correlations with each other, it would seem more natural to formulate it as $p_t(x, a| x_1, a_1, p)$. Is there a reason why this approach is difficult? The text simply explains that it follows [3] without specific justification, which seems insufficient beyond merely following prior work.\n- Typo:\n    - In line 143, it appears that the definition of $\\hat{\\alpha}$ is missing.\n\n[1] Z. Zhang, M. Zitnik, and Q. Liu, “Generalized protein pocket generation with prior-informed flow matching,” arXiv preprint arXiv:2409.19520, 2024.\n\n[2] J. Guan, W. W. Qian, X. Peng, Y. Su, J. Peng, and J. Ma, “3d equivariant diffusion for target349 aware molecule generation and affinity prediction,” arXiv preprint arXiv:2303.03543, 2023.\n\n[3] Y. Song, J. Gong, M. Xu, Z. Cao, Y. Lan, S. Ermon, H. Zhou, and W.-Y. Ma, “Equivariant flow\n407 matching with hybrid probability transport for 3d molecule generation,” Advances in Neural\n Information Processing Systems, vol. 36, pp. 549–568, 2023.\n\nQUESTIONS:\n- Looking at Figure 5, the generation trajectory of the target diff appears much messier compared to the proposed method, yet the sampling efficiency shows slightly better results than the proposed method. How can we understand this phenomenon?\n- What insights might there be if coordinate and atom type probability density paths were modeled jointly rather than separately?\n- Regarding prior guidance, how dependent is the method on prior quality?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper introduces PAFlow, a novel target-aware generative model for structure-based drug design. PAFlow integrates prior protein-ligand interaction knowledge and a learnable atom number predictor into a conditional flow matching framework to generate 3D molecules with improved binding affinity. The model addresses ligand-pocket size mismatches and guides generation toward higher-affinity regions.

STRENGTHS AND WEAKNESSES:
**Strength**  
This paper addresses the problem of undetermined atom numbers by introducing an additional predictor.  
Instead of relying on explicit priors as in previous works, it trains a binding affinity predictor and uses it to guide flow matching sampling toward higher-affinity distributions.
**Weakness**  
The novelty of the flow matching framework is limited, as flow matching has already been applied to SBDD in prior works (e.g., *FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling*, NeurIPS 2024).  
Moreover, although the authors claim their method generates stable molecules of high quality, the visualized results in Figure 3 include unrealistic double bonds. The work should include more results on bond length and bond angle accuracy as evaluation metrics.  
While the method generates molecules with low Vina scores, these scores are even lower than the ground truth from the test dataset, which requires discussion in the paper.

QUESTIONS:
- How did you obtain the binding affinity labels to train the guidance network? How do you ensure the affinities are comparable across different targets?  
- How does the model perform on molecular stability metrics, such as bond length accuracy?  
- How is pocket size calculated in Appendix E.5?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes PAFlow, a novel structure-based drug design (SBDD) model built on the flow matching (FM) framework. The method separately models continuous 3D atomic coordinates and discrete atom types using tailored probability paths and incorporates two key components: a protein-ligand interaction predictor that guides generation toward high-affinity regions, and a learnable atom number predictor that aligns molecule size to the target binding pocket. The method achieves strong results on CrossDocked2020, outperforming many baselines.\n\nSTRENGTHS AND WEAKNESSES:\nThe idea of using interaction prediction to steer the vector field during flow-based generation is clever and effective. It directly targets the main objective of SBDD, tight binding, instead of relying only on general chemical priors.\n\nThe inclusion of a learnable atom count predictor is a surprisingly impactful addition. Most generative models just sample atom counts from empirical priors, which often leads to pocket-size mismatches. Here, by directly predicting atom counts from protein geometry (volume, surface area, atom count), the model creates molecules that better fit into the pocket. The ablations support this clearly.\n\nThe use of conditional flow matching (CFM) for discrete atom types is new and nicely derived. While [8] and others model discrete atoms in diffusion frameworks, this work generalizes the vector field formulation to discrete types in flow matching, which is a non-trivial extension.\n\nOn empirical performance, PAFlow leads across most binding affinity metrics (Vina Score, Vina Min, Vina Dock), outperforming strong baselines like ALiDiff and MolCRAFT. It also achieves good QED and SA scores while preserving diversity. The figure showing that PAFlow achieves best performance on 77% of targets is especially compelling.\n\nThe sampling efficiency is another win. Despite using ODE-based generation, PAFlow is significantly faster than TargetDiff and Pocket2Mol, and even competes with MolCRAFT when run with fewer steps.\n\nThe paper is technically detailed and derivations are thorough. The proofs in the appendix about SE(3) equivariance and the effect of noise injection in the atom number predictor are solid and well-grounded.\n\nWhile the results are strong, the novelty of the base generation framework is incremental. Flow Matching has already been applied in this space (e.g. FlowSBDD), and the main novelty lies in the protein-guided vector field and atom count handling. That’s still a valuable contribution, but not a foundational shift.\n\nThe protein–ligand interaction predictor is trained on normalized binding scores. It would help to discuss how sensitive the model is to this normalization choice, and whether a more realistic affinity predictor (e.g. based on physical interactions) could further improve things.\n\nThe molecular property control (QED, SA, etc.) is not explicitly incorporated but still performs reasonably. Since the authors already use a binding affinity predictor to guide generation, adding similar predictors for QED or SA in future work seems like a straightforward and useful extension.\n\nThere’s no mention of how well this method handles large or irregular binding pockets. A few edge-case visualizations where PAFlow fails or generates oddly-shaped molecules would help clarify model limitations.\n\nRuntime stats are useful, but it might help to report average time per molecule instead of (or alongside) time for 100 samples; just easier to interpret.\n\n[Final Thoughts]: I have raised my score based on the sufficient author responses.\n\nQUESTIONS:\nAll added within \"Strengths And Weaknesses\"\n\nLIMITATIONS:\nAll added within \"Strengths And Weaknesses\"", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}