Title: Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: flow-matching

geometric deep-learning

graph neural networks

differentiable optimization

template-guided docking

computational chemistry

drug-design

3D pose generation
Abstract: Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce a two-step approach for molecular pose generation. Given a template (such as the crystalized version of a ligand), they use flow matching to generate an initial guess for conformations. In a second step, this is then optimized with a differentiable approach that includes biochemical properties (such as the energy) and can also include the binding pocket if the aim is docking. This approach can be seen as a hybrid of a structure-based and ligand-based method. They also introduce a benchmark for template-based docking with broader and more realistic settings.\n\nSTRENGTHS AND WEAKNESSES:\nThe ideas presented in the paper are discussed and demonstrated in detail and an extensive appendix is provided containing a variety of technical details. Generally, the paper is well written and easy to follow, however it took some time to understand the specific flow matching setup they were using and how the model is conditioned on the template. Improving figure 1 might go a long way in making the paper more accessible and easier to digest.\n\nThe main issue I see with the paper is that it is lacking a bit of focus. They introduce three (relatively independent) parts: A flow matching model, a refinement procedure, and the benchmark dataset. While the flow matching part itself is not particularly novel, combining it with the refinement procedure demonstrates improvements is. However, it is not particularly clear / discussed enough how much the flow matching vs refinement contributes to the results. Discussing this in more detail (maybe both parts are equally needed), could help distilling the focus of the work.\n\nApart from that, the authors have discussed their work in detail and especially the discussion in section 5 helps to put the results into context.\n\nQUESTIONS:\n1. The authors reported standard deviations in their main results (table 1), however, they are often quite large and it is not particularly clear whether their method is actually better. It seems like FMA-PO+ is more robust and shows to have a smaller standard deviation. Could you report the actual distributions, e.g., the CDF showing how many ligands have an RMSD of < x? This would make it easier to compare the methods and quantify the results. \n\n2. It was not particularly clear to me how the flow matching sampling works in detail. From figure 2 it seems like the crystallized ligand is used as a conditioning of the NNET that is present across all $t$. However, if this is the case, what target distribution are you using during training? \n\n3. How does each part of your method (i.e., flow matching and refinement) contribute to the overall results? Did you test whether samples generated with your flow matching also improves other energy-based refinement algorithms such as VINA? \n\n4. In Appendix D.5.1 you compared your method with and without pocket information. In table S5, it seems like FMA-PO with pocket seems to perform worse when having pocket information. I would have assumed the score to be better. Could you explain or do you have an intuition for this discrepancy?\n\nLIMITATIONS:\nFor the most part, the authors have adequately discussed the limitations of their work. However, I would have like more discussion about the runtime and whether the performance could be improved for the method to be suitable for large screens.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a FM-based method for template-based ligand docking. First, authors formulate the ligand conformer generation task conditioned on the template 3D structure with flow matching. Next, authors propose the pose optimisation (PO) algorithm that refines the generated pose using shape and colour similarity and additional pocket constraints. Authors evaluate the method on a newly curated  benchmark AlignDockBench and show that the proposed method outperforms other structure-based and ligand-based approaches.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths: clear formulation and presentation, new benchmark, good results (in terms of RMSD).\n- Weaknesses: apparently low quality of the generated structures and insufficient ablation studies (see Questions).\n\nQUESTIONS:\n1. My main concern is about the quality of the generated molecules. While I agree that the ultimate metric in this task is RMSD, I believe that the high quality of the designed conformations is the necessary condition to meet - even before computing RMSD. Besides, the proposed PO has multiple objectives some of which are directly connected to the molecule quality (i.e., steric clashes and strain energies). Based on this, I have two comments:\n - (1.1) In my opinion, the work would benefit a lot from an additional subsection where the authors focus on the quality metrics of the generated poses: i.e. what are the distributions of strain energies for FMA and FMA-PO? how do they compare with the reference molecules and with other methods? same for steric clashes. For future work I would also recommend to look at GenBench3D (https://arxiv.org/abs/2407.04424) - I find it very neat for measuring the 3D quality of generated molecules.\n - (1.2) I see that in the Appendix authors report PB-validity values and they are not very high. I would suggest to focus on these problems and incorporate additional terms in the refinement objective that would improve these values.\n\n2. The proposed PO consists of four terms but I found ablation studies only for the pocket term. I would suggest to do ablation studies for all four to clearly understand the importance of each of them.\n\n3. How strong is contribution of S_Vina in the final S_Score?\n\n4. How is pharmocophoric complementarity between protein and ligand computed? For example, a hydrogen bond between protein and ligand would be represented by a pair of \"complementary\" pharmacophores. But depending on the relative orientation of these pharmacophores a hydrogen bond may or may not exist.\n\n5. Why is protein pocket input always labeled as optional? To me pocket looks like an essential component for this task.\n\n6. How were the weights in Table S2 tuned?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a two-stage framework called FMA-PO, which combines a flow-matching-based model for template-guided ligand pose generation with a differentiable pose refinement module. The method aligns a 2D query molecule to a 3D reference ligand using flow matching, and then refines it based on shape, pharmacophore, internal energy, and optional pocket information. The authors release AlignDockBench, a benchmark dataset specifically for template-based pose generation, containing 369 protein-ligand pairs with co-crystallized templates. This is a valuable contribution on its own, as template-based evaluations are often missing in public datasets.\n\nSTRENGTHS AND WEAKNESSES:\n- The model design is clean and modular. The use of a harmonic prior and graph-based encoding of atom and fragment features allows smooth integration into the FM formulation. This feels like a natural extension of recent works like FlowDock and DiffDock, but with the added benefit of using template ligands instead of receptor pockets.\n\n- The differentiable optimization step is thoughtfully designed. It balances pharmacophore similarity, shape matching, and internal energy terms, which are all differentiable with respect to coordinates. The ability to optionally incorporate protein pocket info is a nice touch and makes the model flexible across use cases.\n\n- Empirical results on AlignDockBench show consistent improvements over several baselines, including FitDock, LS-align, and traditional docking tools like Vina and rDock. The paper reports both mean RMSD and % of poses under 2Å, which are standard and appropriate.\n\n- The improvement in low-similarity and high-flexibility regimes is especially noteworthy. Most alignment tools tend to fail in these settings, and the method's robustness here makes it practically useful.\n\n- The FMA-PO+ variant, where all samples are refined before ranking, offers stronger performance but at the cost of runtime. This tradeoff is acceptable, and it's good that the authors report both variants.\n\n- One thing that's missing is a comparison to recent flow-based or diffusion-based methods like HarmonicFlow or SE(3)-FM. These are the most relevant generative baselines for 3D generation, and even a small subset comparison would help contextualize FMA's performance.\n\n- The use of a Vina-based score in the final scoring function (Sscore) might be a bit confusing, given that Vina was also used as a baseline. It would be good to clarify if this introduces any advantage or bias when comparing methods.\n\n- The paper notes that all runs were done on T4 GPUs and provides runtime comparisons, which is appreciated. Still, there's no mention of how model performance scales with ligand size, does runtime blow up with more rotatable bonds or heavy atoms?\n\n- The benchmark design seems solid, but it would be great to release a few more challenging cases or outlier visualizations, especially where the method succeeds but others fail. This would highlight the strengths of the alignment stage more clearly.\n\nQUESTIONS:\nAll mentioned in \"Strengths And Weaknesses\"\n\nLIMITATIONS:\nAll mentioned in \"Strengths And Weaknesses\"", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FMA-PO, a two-step method for ligand 3D pose prediction using flow matching for initial alignment and differentiable optimization for pose refinement. It introduces a new benchmark (AlignDockBench) and shows competitive results compared to docking and alignment baselines.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n* Tackles an important problem in drug design: predicting ligand poses in the presence of a known co-crystallized template. \n* Provides a new benchmark dataset, which could be valuable to the community. \n* The proposed method shows good experimental results on challenging cases with low template similarity.\n\nCons:\n*  The FMA module is trained without incorporating protein structural information, yet the benchmark labels are derived from co-crystallized complexes that inherently involve protein context. This discrepancy could introduce bias or limit generalizability. A discussion of this mismatch—and potentially experiments adding protein context into FMA—would be useful.\n*  The proposed method involves two stages: ligand alignment and pose optimization. However, the contribution of each stage to the overall performance is not quantified. I encourage the authors to include ablation experiments to isolate the benefit of each stage.\n\nQUESTIONS:\nMy main concern is on the insufficient analysis of two components: the alignment module and the pose optimization module.\n\nFrom my perspective, a key open question is whether the proposed neural network-based ligand alignment outperforms traditional alignment strategies. It would be helpful to compare with classical or widely used alignment methods, such as:\n* KCOMBU [1]\n* The shape alignment algorithm used in FitDock [2]\n* Schrödinger’s shape-based alignment approach [3]\nEven if direct benchmarking is not feasible for all of them, some discussion or rationale for the neural approach (e.g., accuracy vs. speed trade-offs) would strengthen the paper.\n\nTo further assess the value of the proposed pose optimization (PO) stage, a simple but informative experiment would be to use the alignment generated by the current method, followed by pose refinement using FitDock's optimization network (or vice versa). This would help evaluate the robustness and modularity of each stage.\n\nMisc:\n*  The paper mentions modules like MHAwithEdgeBias and PointSetAttentionwithEdgeBias but doesn’t explain them in enough detail. Brief architectural or pseudo-code descriptions would help.\n*  Some hyperlinks in the manuscript are invalid or incomplete. Please check and fix them for clarity.\n*  A relevant citation is missing: the authors should consider citing GroupBind [4], which is very relevant to template-guided modeling.\n\nOverall:\n\nWhile the direction of the work is promising, the current version lacks sufficient analysis of its components and omits comparisons to standard baselines. The contributions are somewhat difficult to isolate. With additional analysis and explanation, I would be more inclined to support acceptance in the future.\n\n-----\nReferences:\n\n[1] Kawabata, T., & Nakamura, H. (2014). 3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity.\n\n[2] Yang, X., Liu, Y., Gan, J., Xiao, Z. X., & Cao, Y. (2022). FitDock: protein–ligand docking by template fitting. Briefings in Bioinformatics, 23(3).\n\n[3] Sastry, G. M., Dixon, S. L., & Sherman, W. (2011). Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. Journal of Chemical Information and Modeling, 51(10), 2455–2466.\n\n[4] Guan, J., et al. (2025). Group ligands docking to protein pockets. ICLR 2025.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}