Title: ForceFM: Enhancing Protein-Ligand Predictions through Force-Guided Flow Matching
Venue: NeurIPS 2025
Accepted: yes
Keywords: Molecular Docking; Flow Matching; Energy-Guided Sampling
Abstract: Molecular docking is a fundamental technique in structure-based drug discovery, playing a critical role in predicting the binding poses of protein-ligand complexes. While traditional docking methods are generally reliable, they are often computationally expensive. Recent deep learning (DL) approaches have substantially accelerated docking and improved prediction accuracy; however, they frequently generate conformations that lack physical plausibility due to insufficient integration of physical priors. To deal with these challenges, we propose ForceFM, a novel force-guided model that integrates a force-guided network into the generation process, steering ligand poses toward low-energy, physically realistic conformations. Force guidance also halves inference cost compared with the unguided approaches. Importantly, replacing the guiding potential with diverse energy functions-including Vina, Glide, Gnina, and Confscore-preserves or improves performance, underscoring the method's generality and robustness. These results highlight ForceFM's ability to set new standards in docking accuracy and physical consistency, surpassing the limitations of previous methods. Code is available at \url{https://github.com/Guhuary/ForceFM}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce ForceFM, a force-guided flow matching model for intrinsic molecular docking. Energy functions guide ForceFM's sampling of ligand structural conformations, yielding notably more accurate poses than (comparable) baseline methods. This work highlights how physics-based knowledge can be incorporated into generative flow models to enhance prediction efficacy.\n\nSTRENGTHS AND WEAKNESSES:\n**Points of strength:**\n1. The authors are among the first to demonstrate theoretically and empirically the strength of combining physics-based knowledge and generative flows for molecular docking.\n2. The authors' empirical results for ForceFM are promising.\n3. The authors compare their method to several well-known baselines using well-known benchmarks.\n\n**Points for improvement:**\n1. The authors' methodological novelty is noted yet modest. The main contribution of this work seems to be in theoretically characterizing how to guide generative flow models with additional energy-based terms. Otherwise, the rest of the network architecture is nearly identical to DiffDock (and its flow-based successors such as PepFlow [1]).\n2. The authors' benchmarks could be made more rigorous concerning dataset splitting and out-of-distribution evaluation. For instance, the PDBBind 2020 dataset splits of EquiBind [2] (which the authors use in this work) are well-known yet unfortunately do not control for protein-ligand interaction similarity with a high degree of stringency. The PoseBusters Benchmark dataset is better in this regard, but it is still not perfect. The authors should also consider evaluating their method on more recent protein-ligand interaction benchmarks such as DockGen [3] to more rigorously characterize the generalizability of their proposed method.\n3. There are a few typos in the paper that should be corrected. A grammar checker should be able to screen these out rather quickly.\n\n**References:**\n\n[1] Li, J., Cheng, C., Wu, Z., Guo, R., Luo, S., Ren, Z., ... & Ma, J. Full-Atom Peptide Design based on Multi-modal Flow Matching. In Forty-first International Conference on Machine Learning.\n\n[2] Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R., & Jaakkola, T. (2022, June). EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. In International Conference on Machine Learning (pp. 20503-20521). PMLR.\n\n[3] Morehead, A., Giri, N., Liu, J., & Cheng, J. Deep Learning for Protein-Ligand Docking: Are We There Yet?. In ICML 2024 AI for Science Workshop.\n\nQUESTIONS:\nHow much time and space complexity does the authors' proposed energy term integration add on top of their base network architecture and sampling algorithm?\n\nLIMITATIONS:\nThe authors' current benchmarks do not evaluate robust protein-ligand interaction generalization beyond the PDBBind training set. Including additional benchmark datasets (besides the PoseBench Benchmark set) would alleviate this concern.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel protein-ligand docking framework named ForceFM. The method is based on the Flow Matching generative model and innovatively incorporates a force-guided network, which leverages multiple classical energy scoring functions (such as Vina, Glide, etc.) as physical priors to guide the ligand conformation generation process. Extensive experiments demonstrate that this approach outperforms current state-of-the-art methods in terms of docking accuracy, physical realism, and sampling efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1.  The core idea of this work—integrating a learnable, explicit force-guided network into the flow matching framework—is novel and significant. It directly addresses the critical issue of insufficient physical plausibility in generated conformations, a common limitation of current deep learning-based docking methods.  \n\n2.  The experimental results are compelling. ForceFM demonstrates outstanding performance in docking accuracy (RMSD) on both standard test sets and unseen proteins. Notably, in the PoseBuster physical validity test, the introduction of the force-guided approach significantly improves the pass rate.  \n\n3. ForceFM can flexibly integrate various energy/scoring functions (Vina, Glide, Gnina, Confscore) and has successfully applied this strategy to another well-known model, DiffDock, achieving performance improvements. This strongly validates the method's generality and robustness.\n\n\n\nCons:\nWhile the work presents significant contributions, its credibility is hampered by several weaknesses in methodological rigor, transparency, and clarity of presentation. I strongly recommend that the authors address the following points in a revision:\n\n1. The paper's primary contribution appears to be the development of a generalizable, plug-and-play energy guidance module. However, the novelty of the underlying baseline model (a flow-matching approach for ligand docking) is somewhat limited. The application of flow-matching models to molecular generation and docking is an active area of research, and the proposed baseline shares conceptual similarities with other recent works. It is suggested that the authors reframe their contribution to more heavily emphasize the guidance module, which is arguably the more significant and novel aspect of this work.\n\n2. The comparison of average runtimes in Table 1 is a significant point of concern regarding methodological fairness.\n - Inconsistent Sampling Steps: The paper does not explicitly state whether the sampling steps were held constant when comparing the baseline models (e.g., DiffDock, 'Ours') with their force-guided counterparts. Section 4.2.4 suggests that different step counts were used, corresponding to the convergence point of each model.\n\n3. Ambiguous Treatment of Guidance Functions.\nThe paper's treatment of guidance functions, particularly GNINA, as simple \"black boxes\" can be problematic. As noted, GNINA is a hybrid method integrating classical scoring functions and a CNN-based model. The paper does not specify which component or version of the GNINA score was used. This lack of specificity makes it difficult to attribute the performance gains to a particular type of physical or learned prior. A more detailed description of the exact scoring function used for each guidance type would allow for a more nuanced understanding of why certain functions perform better than others.\n\nQUESTIONS:\n1. The methodology section suffers from missing definitions for key parameters, which hinders reproducibility.\n    - The variance $\\sigma_{tr, max}^{2}$ for the initial translational sampling (Section 3.2.1) is introduced in the formula, but its numerical value is not provided anywhere in the paper or its appendices.\n    - While the meaning of m in $SO(2)^m$ (the number of rotatable bonds) is mentioned in the supplement, it would benefit from a more direct definition in the main text when the term is first introduced (Section 3.2) for improved clarity.\n\n2. As the core algorithm, the description of Algorithm 1 lacks important details.\n    -  The \"randomly perturb `x_1`\" operation is not specified, leaving the nature and magnitude of the perturbation ambiguous.\n    - Furthermore, the hyperparameter `K` (the number of perturbation samples) is critical to this algorithm. While its choice is justified in an appendix, its importance warrants at least a brief mention of its value and purpose in the main text to ensure the methodology is self-contained.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ForceFM, a novel force-guided flow matching model for protein-ligand docking. The method first trains a purely data-driven flow matching model, and then trains a separate guidance model to apply force-guided corrections based on an energy function. By injecting physics-based knowledge into an advanced generative model, ForceFM is designed to favor conformations that are lower in energy and more chemically plausible. The model is built on sound principles and features an elegant design. Experimental results demonstrate that ForceFM significantly outperforms existing methods in both accuracy and the validity of its generated poses, while also showing strong generalization capabilities. The work has high potential for practical applications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.\tThe paper is exceptionally well-written. It provides a comprehensive and lucid exposition of the underlying principles.\n2.\tThe proposed model is elegantly designed and methodologically sound.\n3.\tThe model demonstrates state-of-the-art performance and shows significant promise for practical applications.\n\nWeaknesses:\n1.\tThe core novelty appears somewhat limited, as the method's success is heavily contingent upon the availability of a high-quality energy function.\n2.\tThe need to carefully tune two key hyperparameters may limit the model's broader applicability and future potential.\n3.\tThe paper lacks a thorough analysis or ablation studies for its key hyperparameters.\n4.\tThe experimental comparison could be more comprehensive, as the set of baselines is somewhat limited.\n\nQUESTIONS:\nThis is good work, and I commend the authors for their contribution. I have a couple of questions to help contextualize the results:\n\n1.\tThe two hyperparameters, the energy weight k during training and the guidance strength η during inference, appear to be crucial for the model's performance. How were their final values chosen, and what trade-offs were considered? I would recommend adding an ablation study to clarify their impact and justify the selection.\n2.\tGiven that the model consists of two trained components, what is its inference efficiency? It would be valuable to report the inference time and, if possible, compare it with the baselines to better assess its practical applicability.\n3.\tAre there other relevant baselines that could be considered for comparison? A broader comparison would help to better position the performance of ForceFM within the field.\n4.\tCould you further elaborate on the practical utility of the model?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper describes an approach to integrate energy functions into a flow matching model for molecular docking.  The energy function is introduced through a force network that does not require ground truth forces, just energies, for training. The evaluations show this approach results in better, more realistic docked poses. The approach is effective across multiple energy functions and model architectures. Claims to generalizability are problematic, however.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths.  This is a good idea. The force network provides a general approach for integrating an arbitrary energy function.  The evaluations convincingly show that including force matching is beneficial. If it weren't for the unsupported claims about generalizability, this would be a solid paper.\n\nThe effect of force guidance on the number of sampling steps needs is an interesting observation.\n\n\nWeaknesses.  The claims to generalizability are overstated and the evaluations do not support the claims. It is true the approach generalizes to multiple energy functions and other generative models (DiffDock), but the paper claims they address \"concerns about overfitting to specific protein or ligand types.\"  No practitioner of molecular docking would agree that time splits are a useful approach to measure  generalization or that simply filtering by Uniprot identifiers is sufficient to remove train/test information leakage.  \nPlease review https://arxiv.org/html/2402.18396v1 for a more rigorous and reasonable approach for accurately assessing generalization.\n\n\nThe model does not actually perform blind docking, but instead uses P2Rank to identify pockets and then does pocket-based docking. A more sensible comparison would be to do pocket-based docking across the board to factor out the influence of P2Rank.\n\nQUESTIONS:\nHow does the method perform in a more realistic cross-docking scenario?\n\nHow sensitive is the model to the initial pocket definition?\n\nIs the graph structure updated with each iterative update (since the distances between nodes will change)?\n\nWhy does table 2 say it is for flexible docking? Nothing in the text indications flexible docking (moveable receptor) is being performed.\n\nThe importance of integrating the force network into training isn't evaluated.  If one simply performed minimization on the generated poses using the evaluated tools, would you see similar improvements in accuracy?\n\nVina does not measure the chemical stability of a conformer, only of its interaction - please be careful with your language.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}