Title: FIGRDock: Fast Interaction-Guided Regression for Flexible Docking
Venue: NeurIPS 2025
Accepted: yes
Keywords: Flexible Docking

Protein-ligand interactions
Abstract: Flexible docking, which predicts the binding conformations of both proteins and small molecules by modeling their structural flexibility, plays a vital role in structure-based drug design. Although recent generative approaches, particularly diffusion-based models, have shown promising results, they require iterative sampling to generate candidate structures and depend on separate scoring functions for pose selection. This leads to an inefficient pipeline that is difficult to scale in real-world drug discovery workflows. To overcome these challenges, we introduce FIGRDock, a fast and accurate flexible docking framework that understands complicated interactions between molecules and proteins with a regression-based approach. FIGRDock leverages initial docking poses from conventional tools to distill interaction-aware distance patterns, which serve as explicit structural conditions to directly guide the prediction of the final protein-ligand complex via a regression model. This one-shot inference paradigm enables rapid and precise pose prediction without reliance on multi-step sampling or external scoring stages. Experimental results show that FIGRDock achieves up to 100× faster inference than diffusion-based docking methods, while consistently surpassing them in accuracy across standard benchmarks. These results suggest that FIGRDock has the potential to offer a scalable and efficient solution for flexible docking, advancing the pace of structure-based drug discovery.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FIGRDock, a regression framework for one-shot flexible protein-ligand docking. The core idea is a two-stage training strategy. First, the model is pre-trained on large datasets  to learn conditional pair representations. Second, this pre-trained model is fine-tuned on a smaller set of high-quality experimental crystal structures (PDBbind).  Tested on the PDBbind and PoseBusters, FIGRDock outperforms baselines such as FlexDock and ReDock.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**：\n1. The paper is generally well-written and easy to follow.\n2.  FIGRDock outputs baselines (such as FlexDock, ReDock, and Smina) on the PDBbind and PoseBusters test set\n\n\n**Weaknesses**:\n1. Lack of Technical Originality. The strategy of pre-training on large datasets of computationally docked structures has been explored in prior work (HelixDock, CarsiDock). The pre-training strategy of ligand encoder and pocket encoder are also previous Uni-Mol and ProFSA.\n2. The paper omits several highly relevant and competitive baselines in the flexible docking space. For example, DiffBind-FR.\n3. There is a major concern regarding the unfair comparison of the reported results. The model is pre-trained on the SIU dataset, which is derived from structures in the PDB. The PDBbind test set is a time-split subset of the PDB. It is highly probable that the molecular systems (or highly similar ones) in the PDBbind test set are present in the massive SIU pre-training dataset. While the authors use a time-split for fine-tuning, they do not mention any filtering of the pre-training data based on the test set. This data contamination would mean the model has essentially \"seen\" the test examples during pre-training, making the strong performance unsurprising and casting doubt on the model's true generalization ability to novel targets.\n\nQUESTIONS:\n**Suggestions/Questions:**\n1. Providing a similarity analysis between pre-training dataset and testing set.\n2. Providing a comparison between more powerful baselines, such as DiffBind-FR and HelixDock.\n3. Besides the task, what are the main difference between FIGRDock and previous pre-training docking methods, such as CarsiDock?\n\nI'm willing to raise my score if these **suggestions/questions** are solved.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a regression-based approach, FIGRDock, for pocket-based flexible docking. Key to the success of regression-based approaches is a precise understanding of protein-ligand interactions and associated distances, which are then used by the structure module to then predict the final structure. The precise understanding of protein-ligand interactions is achieved through a novel pre-training stage, utilizing pocket-reconstruction and distance-map prediction tasks. Empirical results showcase the improved performance of FIGRDock over previous baselines.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**:\n\n* Accurately modelling protein flexibility in docking is an important yet understudied problem. The paper takes a step in the right direction in that regard, with a very computationally efficient method.\n\n* The submission is clearly written, well-organized, and the technical details and contributions of the paper are well-explained to the reader.\n\n* The technical contributions / choices adopted by the paper are sound, especially in the context of flexible docking. To my best knowledge, pre-training a pocket-encoder for docking with a denoising task is novel, even though similar strategies have been used for small molecule representation learning. Empirical investigations confirm the effectiveness of these choices (some caveats remain, see Weaknesses).\n\n\n### **Weaknesses / Clarifications:**\n\n**Technical Contributions:**\n\n1. The main technical contribution of the paper lies in the conditional pre-training stage, where both the pocket encoder, and pair-representation between the ligand and protein is learnt.\n\n**Question**: For the pocket encoder denoising task, do you only use a single noise scale, or is it a combination of noise scales as in diffusion models? \n\n2. For the distance-map prediction task, it appears as though a regression paradigm is used by directly predicting distances. A commonly used strategy across co-folding approaches is the distogram prediction which insteads utilizes a classification perspective.\n\n**Questions:**\n\n* Did the authors experiment with both regression and classification settings, and if yes, have any insights on when one paradigm would be preferred over the other?\n* How do the authors ensure training is stable across different distance scales in the regression setting?\n\n**Empirical Results:**\n\n1. From the ablation results, it is quite clear that the greatest improvement in performance comes from the conditional pre-training strategy (~9% improvement). This improvement emerges from one of the following reasons:\n\n* The pre-training data from ProSFA and SIU datasets contain a lot of diversity in pocket structures and binding modes, and allow the model to learn flexible interaction patterns, improving generalization.\n\n* The interaction patterns in the pre-training data largely mimic patterns seen in the test set (this is a known issue with the time-split of PDBBind), and the increased performance is from additional memorization. \n\nIn my experience, such large improvements are often from sort of memorization rather than true generalization. Given the mounting evidence of similar issues also for co-folding models such AlphaFold3 and Boltz-1 [1], my suspicion for the improved performance is from the latter reason. \n\nThis is a critical issue as the main utility of docking models is to provide fast and accurate predictions across the proteome. If the conditional pre-training stage only offers a better route to memorization, the technical contributions of the paper are immediately thin.\n\n**Question:** Is it possible to construct an overlap score similar to [1], between the 5 million poses in SIU dataset and the PDBBind split test set? The distogram of the similarity to the closest training set point should give a clear indication of whether memorization was happening.\n\n2. For flexible docking, one is also often interested in predicting accurate protein structures, given the downstream utility in binding affinity prediction.\n\n**Question:** Can the authors also compare the pocket-aligned All-Atom RMSD < 1 of their method against other baselines (wherever available)? This should help understand if the pocket-encoder pre-training endows the model with some understanding of the protein conformational space.\n\n3. The learnt interactions are only evaluated from a geometric perspective.\n\n**Question:** Can the authors also evaluate the protein-ligand interaction recovery metrics from [2] to see if key physico-chemical patterns are preserved?\n\n4. This is minor, but most docking papers claim that fast approaches are needed to support the demands of virtual screening. However, there are a few papers that showcase the effectiveness of active-learning style approaches in finding reasonable hits for virtual screening, which makes computational efficiency (in comparison to accuracy) a less of a bottleneck [3, 4]\n\nI have currently recommended the paper as Borderline Reject. If there is very little evidence for memorization from the pre-training stage, I am happy to raise my score. On the other hand, if the evidence is overwhelmingly in favor of memorization, I will keep my rating.\n\n**References:**\n\n[1]: Have protein-ligand co-folding methods moved beyond memorization?\\\n[2]: Assessing Interaction Recovery of Predicted Protein-Ligand Poses\\\n[3]: Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases\\\n[4]: New Trends in Virtual Screening\n\nQUESTIONS:\nQuestions are marked with a **Questions** identifier in the Weaknesses section.\n\nLIMITATIONS:\nLimitations are not discussed, and should be included. For example, generative models align well with the experimental uncertainty associated in the docking task, and have an implicit ability to correct their predictions. This is however missing in regression based models.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a regression-based method for flexible docking, pretrained on computational data and fine-tuned on crystal data. The method achieves comparable results in terms of both accuracy and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.  This work applies a regression-based method, typically used for rigid docking, to the task of flexible docking and achieves results comparable to those of diffusion-based models, offering a new alternative for future research.\n2.  This work provides the insight that a \"one-shot approach demands precise interaction modeling\" and proposes learning this interaction through a pre-training paradigm on data generated by docking software.\n3.  This work significantly improves the efficiency of flexible docking prediction compared to diffusion-based models, making it more practical for real-world applications.\nWeaknesses:\n1.  The novelty of this work is limited, as the primary innovation is the \"Conditional Pre-training\" process, with no modifications to the prediction framework itself.\n2.  Section 3, which details the FIGRDock method, is poorly written. For example, it lacks formal mathematical equations and structural details for key modules such as the \"Contact Module\" and \"Regression-based Docking Module,\" providing only the loss functions.\n3.  The performance of FIGRDock is highly reliant on the SIU pre-training set, while the baseline models were trained only on PDBbind. This discrepancy in training data leads to an unfair comparison.\n\nQUESTIONS:\n1.\tSection 3 would be strengthened by a more detailed explanation of the modules. Could the authors elaborate on their implementation or architecture?\n2.\tFor a fair and direct comparison, the baseline models should be trained and evaluated on the same datasets as FIGRDock. The current comparison is weakened by this discrepancy.\n3.\tFigure 1 appears to be redundant, as it repeats information already present in Figure 2. Additionally, the framework diagram should be annotated with the mathematical notations for each module (e.g., labeling the \"Ligand Coordinate Head\" with f_lc) to improve clarity and connect the diagram to the text.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FIGRDock, a new flexible docking framework pretrained on synthetic protein-ligand complexes and fine tuned to complexes derived from laboratory experiments.  FIGRDock takes apo pocket and ligand as inputs and uses regression to match the intra-ligand and intra-protein changes as well as the interfacial interactions between protein and ligand.  The resulting trained model is much faster and more accurate than typical diffusion-based or search-based docking methods, though less accurate than AF3 and its variants.\n\nSTRENGTHS AND WEAKNESSES:\nThis paper demonstrates that a simple, MSA-free approach of machine-learned docking can reach high performance, competitive with traditional software used in drug discovery.  This is an important recent area of research and the solution by the authors is notably faster during inference than any of the previous flexible docking approaches.  The presentation of the design of the model and its training protocol are clear.\n\nThe main weakness of this work relates to the challenge in evaluating it for its intended purpose: helping drug discovery.  Although the results on time-split datasets are useful and desirable for the sake of comparison to past approaches, it would be useful to also present different, harder data splits and, perhaps most importantly, to stratify the evaluation by similarity to the training set data, for example as done in Fig 1A,B,C of Škrinjar et al, 2025 (https://doi.org/10.1101/2025.02.03.636309).  This is critical, given that AF3 (superior to FIGRDock in accuracy though much slower) struggles with novel molecules and pockets that matter the most in practical contexts.  Addressing this generalization gap would significantly improve this work.\n\nQUESTIONS:\nCould the authors also run GOLD/VINA starting with apo structures in figure 2?  Also, why use ESMFold and not AF2 for the apo structures---is that simply a result of the analysis in paragraph 4.2.2 or were there additional considerations?\n\nCould the authors test their hypothesis in page 9 paragraph 2 about the origin of the lack of generalization for the model trained on AF2 structures by perhaps augmenting the dataset with artificially noised structures?\n\nWhat is the x-axis notation in Fig 4?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}