Fast and Accurate Blind Flexible Docking

Authors: Zizhuo Zhang, Lijun Wu, Kaiyuan Gao, Jiangchao Yao, Tao Qin, Bo Han
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Blind Flexible Molecular Docking, Structure Prediction, AI4Science, Ligand-Protein Graph

Abstract:
Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets, plays a vital role in drug discovery. However, existing docking methods often face limitations: they either overlook crucial structural changes by assuming protein rigidity or suffer from low computational efficiency due to their reliance on generative models for structure sampling. To address these challenges, we propose FABFlex, a fast and accurate regression-based multi-task learning model designed for realistic blind flexible docking scenarios, where proteins exhibit flexibility and binding pocket sites are unknown (blind). Specifically, FABFlex's architecture comprises three specialized modules working in concert: (1) A pocket prediction module that identifies potential binding sites, addressing the challenges inherent in blind docking scenarios. (2) A ligand docking module that predicts the bound (holo) structures of ligands from their unbound (apo) states. (3) A pocket docking module that forecasts the holo structures of protein pockets from their apo conformations. Notably, FABFlex incorporates an iterative update mechanism that serves as a conduit between the ligand and pocket docking modules, enabling continuous structural refinements. This approach effectively integrates the three subtasks of blind flexible docking—pocket identification, ligand conformation prediction, and protein flexibility modeling—into a unified, coherent framework. Extensive experiments on public benchmark datasets demonstrate that FABFlex not only achieves superior effectiveness in predicting accurate binding modes but also exhibits a significant speed advantage (208$\times$) compared to existing state-of-the-art methods. Our code is released at~\url{https://github.com/tmlr-group/FABFlex}.

Decision: accept (poster)
Reviewer scores: 8, 6, 6, 8

Reviews and discussion:
<thread>
creation date:
1729577743678

thread_type:
official_review

<thread_object>
creator: reviewer_r36f

<content>
summary:
This work proposes FABFlex, a regression-based model for fast and accurate docking of protein-ligand pairs. The pipeline of blind flexible docking is decomposed of three stages: 1) predicting the binding pockets, 2) predicting the holo structure of ligand and 3) predicting the holo structure of receptor pockets. An iterative update mechanism is utilized for continuous structure refinement. Solid experimental results verify the superiority of FABFlex in terms of ligand prediction, pocket prediction and inference efficiency.

strengths:
1. At a time when diffusion is prevalent, the work verifies the efficacy of iterative update mechanism for structure refinement, which achieves extremely high efficiency and better experimental results.
2. Very solid experimental results are reported to show the superiority of FABFlex, including multiple tasks of interest in blind docking and various strong baselines.

weaknesses:
1. Although good experimental results were achieved, the innovation of this work is relatively insufficient (e.g., using existing network architectures and training objectives), which is only reflected in the proposed iterative update mechanism.
2. In some tasks, the results of FABFlex are worse than those of some models based on protein rigidity prior (e.g., pocket prediction performance in Table 2). This anomaly requires further explanation.

questions:
1. In Table 2 we see that FABind and FABind+ achieve best results on some metrics, which seems somewhat counterintuitive since they are tailored based on the assumption of protein rigidity. Could you please give futher explanations of this phenomenon?
2. In Table 3, the ablation of "iterative internally" shows better results on mean and median of ligand RMSD. I wonder if there is room for further improvement in protein-ligand interaction modeling.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730194765515

thread_type:
official_review

<thread_object>
creator: reviewer_w792

<content>
summary:
This paper introduces FABFlex, a framework for fast and accurate blind flexible docking. It consists a pocket identification module (blind), a ligand conformation prediction module (docking), and a protein flexibility modeling module (flexible).  FABFlex achieves sota results on  blind flexible docking benchmark, and is 208x faster than previous sampling-based deep learning methods.

strengths:
1. The paper is well-written, with clearly designed figures and thorough explanations of each component.

2. The experiments, ablation studies, and visualizations are comprehensive and well-detailed.

3.  The framework demonstrates strong performance and significantly faster inference times compared to sampling-based approaches.

weaknesses:
1. The primary concern is that this work appears to be a direct application of the FABind series. It just introduces an additional pocket conformation prediction module to handle the flexible docking setting, which limits the overall contribution and novelty of the paper.

questions:
1. Why does this paper focus solely on blind (global) flexible docking, instead of exploring pocket-based (local) flexible docking? It seems feasible to adapt the framework to a local flexible docking setting and compare it against models such as DiffDock-Pocket[1], ReDock[2], FlexPose[3], and DiffBindFR[4]. 

[1] Plainer, Michael, et al. "DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility." NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development.

[2] Huang, Yufei, et al. "Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge." Forty-first International Conference on Machine Learning.

[3] Dong, Tiejun, et al. "Equivariant flexible modeling of the protein–ligand binding pose with geometric deep learning." Journal of Chemical Theory and Computation 19.22 (2023): 8446-8459.

[4] Zhu, Jintao, et al. "DiffBindFR: an SE (3) equivariant network for flexible protein–ligand docking." Chemical Science 15.21 (2024): 7926-7942.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730664857737

thread_type:
official_review

<thread_object>
creator: reviewer_h5gn

<content>
summary:
In this paper, the authors introduce FABFlex, a regression-based flexible blind docking model. FABFlex uses the EGNN architecture, with FABind as its main layer. The model is composed of three modules: (1) the pocket prediction module, (2) the ligand docking module, and (3) the pocket docking module. FABFlex focuses on finding holo structures to enable flexible docking. Compared to other models, FABFlex demonstrates accuracy with a higher percentage of ligands achieving RMSD < 2 Å and RMSD < 5 Å, and it is also accurate in identifying holo pockets based on RMSD metrics.

strengths:
- The paper presents all details clearly and comprehensibly.
- This is the one of first applications of regression-based flexible blind docking.
- It employs an interesting multi-task approach, addressing more than a single task simultaneously.
- The use of flexibility, rather than just rigidity, in docking, combined with a regression approach, makes the paper compelling.
- The pipeline provides a systematic, data-driven approach to molecular docking.
- The experiments are detailed and clearly presented, with comprehensive benchmarking against other models.
- The code is openly shared (though the README is empty; see weaknesses).

**Originality:** FABFlex is innovative as a regression-based model for flexible docking. It also stands out for its multi-task capability, predicting not only apo structures but also holo-structures.

**Quality:** The paper's quality is highlighted by its comprehensive benchmarking against other models. Additionally, it goes beyond ligand RMSD to include pocket RMSD analyses, further enhancing the study’s depth.

**Clarity:** The paper is well-written grammatically, with equations that are scientifically clear, readable, and easy to follow.

weaknesses:
- This study closely resembles the FABind[1,2] approach in both training and inference, with many of the techniques used already present in FABind. Consequently, flexible docking appears somewhat overshadowed by FABind.
- It is not specified whether protein preprocessing is used for inference runtime, affecting the runtime comparison.
- Using binary classification to identify pocket regions could limit flexibility at the atomic level; a more adaptable approach may be beneficial.
- The study relies solely on the RMSD metric, which does not always ensure plausible structures. Metrics such as semi-empirical binding affinity, protein-ligand steric clashes, and ligand strain energy, as suggested by [3,4,5,6,7], should be considered.
- Although proteins and ligands are visually classified as apo or holo, this classification does not guarantee bioactive, chemical, or physical plausibility. Including metrics like binding affinity, steric clashes, and ligand strain energy would provide greater insight, as demonstrated in studies like Posebuster[3], PoseCheck[4], PoseBench[5], CompassDock[6], and PLINDER[7].
- The binding affinity, protein-ligand steric clashes, and ligand strain energy for the processed PDBBind’s apo and holo structures were not examined.
- While RMSD may increase due to holo pocket region flexibility, the focus should instead be on binding affinity, steric clashes, and ligand strain energy, but the study only emphasizes RMSD.
- It is unclear if other DL-based models in the benchmark used the same timesplit or were retrained for comparability.
- If RMSD is the chosen metric, timesplit information leakage may occur, as described in the PLINDER study. Clarifying timesplitting methods would strengthen the study.
- As a FABind user, I found the reported runtime of 0.12 sec inaccurate. When protein preprocessing is included, runtime is closer to 10-15 seconds, with 0.12 sec applying only to ligand conformation prediction. Based on FABind, I expect FABFlex’s runtime to exceed the stated 0.49 sec with protein preprocessing included.
- Although FABFlex claims faster runtime than DiffDock, normalizing per-ligand runtime suggests DiffDock may be more efficient, as DiffDock samples ~40 ligand conformations in ~2 sec per conformation, while FABFlex takes ~10-15 sec for one prediction.

**Quality:** The RMSD metric may lack biological, chemical, or physical relevance. For benchmarking, it would be better to include up-to-date metrics like semi-empirical binding affinity, protein-ligand steric clashes, and ligand strain energy.

**Clarity:** The criteria for selecting the values of alphas in the loss function are not clearly explained.

**Reproducibility:** Although the code is open source, the README section is empty, and usage instructions are not provided. If the README were clarified, I could test and reassess the code’s reproducibility. Additionally, the conda environment in the YAML file is named "FABind," raising anonymity concerns regarding double-blind review, as it suggests potential overlap with FABind’s authors.

**Minor Mistake**

"Subsequent" is used twice:
> "The predicted pocket sites by pocket prediction module enable the subsequent subsequent ligand"

### **References**
[1] Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan. FABind: Fast and Accurate Protein-Ligand Binding. arXiv preprint arXiv:2310.06763, 2023.

[2] Kaiyuan Gao, Qizhi Pei, Jinhua Zhu, Kun He, Lijun Wu. FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation. arXiv preprint arXiv:2403.20261, 2024.

[3] Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane.  Posebusters:  Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 15(9):3130–3139, 2024. 

[4] Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell. Benchmarking generated poses: How rational is structure-based drug design with generative models? arXiv preprint arXiv:2308.07413, 2023.

[5] Alex Morehead, Nabin Giri, Jian Liu, Jianlin Cheng. Deep Learning for Protein-Ligand Docking: Are We There Yet? arXiv preprint arXiv:2405.14108, 2024.

[6] Ahmet Sarigun, Vedran Franke, Bora Uyar, Altuna Akalin. CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning. arXiv:2406.06841, 2024.

[7] Janani Durairaj et al. PLINDER: The protein-ligand interactions dataset and evaluation resource. bioRxiv, 2024

questions:
- Do you account for protein preprocessing during inference? Based on my experience with FABind, preprocessing typically takes around 10-15 seconds.
- Does the flexibility in your model prevent steric clashes between protein and ligand?
- Did you use the same timesplitting approach for training other DL-based methods as you did for FABFlex?
- Did you experiment with different pocket site radii, such as 10 Å or 5 Å, in addition to 20 Å?
- Are the alpha values learnable, or do you use a predetermined approach for them?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730687341616

thread_type:
official_review

<thread_object>
creator: reviewer_rase

<content>
summary:
This paper addresses the challenging and realistic scenario of blind flexible molecular docking, where the binding sites are unknown and the proteins exhibit dynamic behavior during the docking process. This is a critical problem that reflects how molecules interact with proteins. The paper argues that current flexible docking methods, predominantly reliant on sampling strategies with diffusion models, suffer from significant inefficiencies. To overcome this drawback, the paper explores the potential of regression-based model in handling flexible docking. Utilizing AlphaFold2-predicted apo protein conformations, this paper proposes an end-to-end regression-based model named FABFlex, designed to achieve both fast computation and accurate docking performance in blind flexible docking scenarios. Experiments show that FABFlex not only significantly enhances the ligand structures and positively performs impacts on pocket conformations, but also substantially accelerates docking speed compared to the recent SOTA method, DynamicBind.

strengths:
1.	The paper tackles the blind flexible molecular docking scenario, which is a more practical and crucial setting compared to many existing studies that focus on rigid docking, where proteins are assumed to be static during the docking process.
2.	The architecture of the proposed model is intuitive and easy to comprehend, with each module specifically designed to address a subtask of the blind flexible docking problem. It is easy to follow.
3.	The model significantly outperforms the SOTA docking methods, such as DynamicBind, DiffDock, and TankBind, on ligand structure predictions. Additionally, it operates much faster than recent SOTA flexible docking method, DynamicBind (approximately 208 times).
4.	This model maintains a robust generalization ability on those unseen proteins. The visualization of the iterative mechanism is very interesting, illustrating how the ligand is gradually docked from the apo to the holo state.

weaknesses:
1.	It is unclear why the number of ligand-protein sample pairs of PDBBind v2020 used in this paper is smaller than that in the existing studies such as TankBind, FABind.
2.	It seems that FABFlex relies on FABind layer as the fundamental component to construct the model, but the details of how this layer is adapted for use in FABFlex are not clearly articulated.
3.	It seems that the model assumes that there is only a single binding pocket in a given ligand-protein pair, whereas in reality, there are possibly multiple potential binding pockets.

questions:
1.	Does FABFlex’s docking result have atomic clash problem? If so, how to mitigate or resolve this problem?
2.	Can you provide more details of the construction and workflow of FABFlex?
3.	Can you use some way to intuitively demonstrate the pocket prediction performance?
</content>
</thread_object>

</thread>
<thread>
creation date:
1733049808202

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General response

comment:
Dear All Reviewers and AC,

We appreciate all your insightful suggestions and valuable comments, which are immensely helpful for us. During the rebuttal phase, we are pleased to receive numerous positive remarks, such as the acknowledgement of realistic significance of blind flexible docking scenario (raSe, H5gN), as well as the recognition of our contribution as the first work to explore the potentials of regression-based methods for flexible docking (H5gN, R36f). The reviewers also appreciate the clarity and well-written quality of our paper (H5gN, w792), as well as the strong and solid experimental results (raSe, H5gN, w792, R36f), particularly achievements in high computational efficiency and improved docking accuracy compared to state-of-the-art methods (raSe, w792, R36f).

More importantly, inspired by the reviewers' comments and the interaction, our manuscript has been continually improved regarding the unclear parts or experimental verification about some specific points. We carefully followed the reviewers' suggestions to include additional experiments in our revised paper. For your reference, we supplement the main additions in the following:

- **New baseline DiffDock-L:** supplemented in Table 1 and Figure 6.  
- **Clash score evaluation:** supplemented in Table 9 and Appendix C.8.  
- **Binding affinity assessment:** supplemented in Table 10 and Appendix C.9.   
- **Analysis of number of samplings**: supplemented in Table 6 and Appendix C.2.  
- **Assessment of PoseBuster test suite:** supplemented in Table 8, Figure 9, and Appendix C.6.  
- **Performance on pocket-based flexible docking:** supplemented in Table 11 and Appendix C.10.  
- **Additional case study of pocket prediction:** supplemented in Figure 13 and Appendix C.12.

Thank you once again for all reviewers' valuable time and efforts. Your thoughtful comments, no matter about the pros or the cons, all have been instrumental in improving the quality of our paper and have inspired us to continue advancing this research.

Best regards,  

Authors of #7007
</content>
</thread_object>

</thread>
<thread>
creation date:
1745925616975

thread_type:
public_comment

<thread_object>
creator: ~anton_ber1

<content>
title:
Unseen proteins results

comment:
Dear authors,

In Table 1 you reported RMSD for complexes on unseen protein receptors, which was not included in PDBBind test split. Could you please clarify, which complexes from PDB (or maybe another data source) was used as unseen protein receptors?
</content>
</thread_object>

</thread>


Metareview:
metareview:
In this submission, the authors propose an effective and efficient blind ligand-protein docking method, which achieves encouraging performance. The proposed method formulates the blind docking problem as a multi-task learning problem, designing three modules to predict pockets, the holo-structures of ligands, and those of protein pockets, respectively, and learning them iteratively. The proposed method is reasonable. In my opinion, it demonstrates the potential of the "bottom-up" strategy in the blind docking problem. The authors resolved the reviewers' concerns successfully in the rebuttal phase, providing more explanations with additional experimental results. Taking all these into account, I decide to accept this work and suggest the authors further merge the content in the rebuttal and discussion phase into the final paper.