Title: DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
Venue: NeurIPS 2024
Accepted: yes
Keywords: Molecular Docking; Binding Pocket Prediction; Contrastive Learning; Iterative Refinement
Abstract: Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning (GDL), have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist. To accommodate various docking settings and achieve accurate, efficient, and physically reliable docking, we propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking. We innovatively reframe the pocket prediction task as a pocket-ligand alignment problem rather than direct prediction in the first stage. Then we follow a bi-level coarse-to-fine iterative refinement process to perform site-specific docking.  Comprehensive experiments demonstrate the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieves a 31\% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model 
DiffDock. With the consideration of physical validity, this improvement increases to about 300\%.

DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking

Abstract

Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning (GDL), have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist. To accommodate various docking settings and achieve accurate, efficient, and physically reliable docking, we propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking. We innovatively reframe the pocket prediction task as a pocket-ligand alignment problem rather than direct prediction in the first stage. Then we follow a bi-level coarse-to-fine iterative refinement process to perform site-specific docking. Comprehensive experiments demonstrate the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieves a 31% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model DiffDock. With the consideration of physical validity, this improvement increases to about 300%. †

1 Introduction

Recent advancement in geometric deep learning (GDL) [1, 2, 3] presents an innovative and promising molecular docking paradigm to predict and understand the interactions between target proteins and drugs, which is of paramount importance for drug discovery [4, 5]. Unlike traditional docking methods that employ optimization algorithms to sample and identify best binding poses [6, 7], GDL methods interpret molecular docking as either a regression or generation task, eliminating the need for intensive candidate sampling [8, 9, 10]. Studies have demonstrated that GDL methods outperform their traditional counterparts, delivering enhancements in both the accuracy of binding pose predictions, as measured by the root-mean-square deviation (RMSD) metric, and the inference efficiency [11, 12].

According to whether a prior pocket is given, molecular docking can be divided into blind and site-specific docking [13]. Traditional sampling methods adeptly navigate both scenarios, primarily differing in the scope of the search space they explore. In contrast, GDL methods typically specialize in either one. For instance, EquiBind [8], and DiffDock [9] are designed for blind docking, neglecting the incorporation of binding pockets. Uni-Mol [14] and DiffBind-FR [15] concentrate on site-specific docking and only protein atomic level structure within a defined radius (usually 6-12 Å) of the

co-crystal is modeled. Despite some progress, these methods not only fail to handle two docking settings smoothly like traditional methods, but also confronted with certain limitations. For blind docking methods, they ignore the fine-grained protein side-chain structure. Regarding the site-specific docking methods, when dealing with pockets larger than the predetermined cutoff or when there is a requirement to model extensive pocket surrounding structures to account for long-range interactions, these methods significantly deteriorate in performance [16] and the demand for computational resources can escalate significantly, as evidenced in Appendix.A.2 and Appendix.A.3.

Besides these challenges, current GDL methods face additional limitations due to the lack of inductive biases, such as penalties for steric clashes or constraints on ligand mobility, leading to the generation of unrealistic docking poses. Buttenschoen et al. [16] proposed the PoseBusters test suit to verify and highlight these problems. In addition to the RMSD between predicted and ground-truth poses, the test suite incorporates 18 checks, encompassing chemical validity and consistency, intramolecular validity, and intermolecular validity. According to the test suite, the previously highest-performing method, DiffDock, achieves a success rate of only 14%. This is significantly lower than the 38% success rate achieved when chemical validity is not taken into account.

To resolve these problems, we propose DeltaDock , a unified GDL framework for accurate, efficient, and physically valid docking. DeltaDock is a two-stage framework consisting of a pocket prediction stage and a site-specific docking stage. With "Delta" , we mean that the optimal poses are predicted by iteratively refining the input structures in the second docking stage. The first pocket prediction stage is specialized for blind docking, where a binding pocket is identified from a set of candidates through a novel contrastive pocket-ligand alignment module CPLA. Then in the second stage, within the pockets predefined or selected by CPLA, binding structures are predicted in a bi-level coarseto-fine iterative refinement module Bi-EGMN. This module prioritizes the residue-level structure covered by a large outer box (Fig.4) for pose positioning and coarse structure prediction. And the atom-level structure, within a relatively small radius from the coarse structure, is characterized for more refined predictions. In particular, the module incorporates (i) a GPU-accelerated pose sampling algorithm generating high-quality initial structure, (ii) a training objective imposing penalties for steric clashes and constraints on ligand mobility, and (iii) a rapid post-processing step composing torsional alignment and energy minimization for structure correction.

To accommodate two different docking settings, DeltaDock is specially designed as a two-stage framework rather than an end-to-end framework. Particularly, the pocket-ligand alignment module is inspired by the observation shown in Fig.5. Existing pocket prediction methods generally achieve a recall rate of just 70%-80%. However, when combining all possible pockets predicted by multiple methods, this recall rate reaches nearly 95%. According to this result, we shift the focus from designing increasingly powerful pocket prediction models to developing strategies for the effective selection of a candidate pocket from an ensemble of predicted pockets. The pocket prediction task is thus reframed as a pocket-ligand alignment problem innovatively. Regarding the site-specific docking stage, the key idea is to accurately predict reliable poses. Based on the proposed bi-level iterative refinement model, several components presented above are introduced additionally. Among them, the pose sampling algorithm is adopted for structure initialization, as previous works on structure prediction [17] have demonstrated the importance of a good initial structure. Other two components, namely the physics-informed training object and the fast structure correction step, are leveraged to ensure physical validity.

To demonstrate the effectiveness of DeltaDock, we performed comprehensive experiments to evaluate its predictive accuracy, efficiency, generalizability, and ability to predict physically valid binding poses. The experimental outcomes indicate that DeltaDock consistently surpasses the baseline methods in both blind docking and site-specific docking settings while maintaining remarkable computational efficiency. Notably, in the blind docking setting, DeltaDock exceeded the performance of the previous SOTA GDL method, DiffDock, by 30.8% in terms of the docking success rate, and it required only approximately 3.0 seconds per protein-ligand pair. With the consideration of physical validity, this improvement increases to approximately 300% on the PoseBusters benchmark.

Encoder

Encoder

Ligand Molecule

…

Contrastive Learning

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b)

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𝑚

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Ligand atom

Protein atom

Protein

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-carbon

Minimum distance to ligand atoms

Boundary of cubic box

Bilevel Iterative Refinement

Fast Structure Correction

2

1

3

Residue-level refinement

Sampled initial pose

Updated pose

SMINA Energy

Minimization

Atomic-level refinement

1

2

Adjusting torsional angles

Rotating ligand

Translating ligand

Final Output

20Å

6Å

Input

Output

Recycling

2 Related Work

2.1 Sampling-based Docking

Traditional docking methods, epitomized by the likes of VINA [18] and SMINA [19], operate on a "sampling-and-scoring" paradigm to identify the best binding pose. Optimization algorithms such as BFGS [20] are used to sample optimal poses within the defined search space on CPUs. This process, which involves a significant number of steps and multiple copies, is rather computationally intensive. Recent studies have attempted to speed up the sampling process using GPUs. Notable examples are Vina-GPU [21], Uni-Dock [22], and DSDP [23], which use more copies and shorter search steps to fully leverage the parallel computational power of GPUs. This approach has demonstrated substantial efficacy, achieving a speed increase of an order of magnitude compared to prior CPU-based methods.

2.2 Geometric Deep Learning-based Docking

GDL introduces a new paradigm in molecular docking, where the sampling process is bypassed by interpreting molecular docking as either a regression task or a generation task [8, 9]. However, recent researches have highlighted limitations of current GDL methods, such as neglect of protein side-chain structures [15], difficulties in handling large binding pockets, and challenges in predicting physically valid structures [16]. Compared with physically reliable sampling-based methods, especially recent developed GPU-accelerated methods, the existing limitations hinder the practical application of GDL methods. To address these concerns, in this work, we propose DeltaDock to overcome these problems and accomplish efficient, accurate, and physical reliable docking.

2.3 Binding Pocket Prediction

As the foundation of structure-based drug design, binding pocket prediction has attracted expansive attention. A variety of methods have been developed for this task, encompassing traditional computational methods, such as Fpocket [24], machine learning (ML) methods, such as P2Rank [25], and GDL methods, such as PUResNet [26]. These methods generally adopt ligand-free approaches and focus on predicting all potential binding sites within individual proteins. Recent blind docking methods, DSDP and FABind, apply pocket prediction for target ligands to reduce the docking search space, which is of great help to fast and accurate blind docking. In this study, our proposed model, DeltaDock, also prioritizes defining a pocket for blind docking. However, instead of improving model architecture for pocket prediction like previous methods, DeltaDock reframe the pocket prediction task as a pocket-ligand alignment problem and employ contrastive learning to select a candidate pocket from the combined pockets set.

3 DeltaDock Framework

3.1 Preliminaries

Notations. In this work, the separate structures of a protein P and a ligand L are used as inputs (Fig. 1). Both molecules are initially encoded as graphs, and we denote a molecule graph as G = ( V , E ) , where V and E represent the node set and edge set respectively. Each node v i ∈ V is associated with a coordinate x i and a feature vector h i . Each edge ( i, j ) ∈ E is associated with an edge feature vector e ij . For the ligand L and ligand graph G L , v L i represents the i -th atom in the ligand and x L i corresponds to the atom's coordinate. For the protein P , the situation is more complex, and two graphs based on the two structural levels of the protein are constructed. One is the protein atomic graph G P , and the other is the protein residue graph G P∗ . G P contains protein atomic-level information similar to ligand graph G L , while G P∗ contains protein residue-level information and overlooks the side-chain structure information. In G P∗ , v P∗ i represents the i -th residue in the protein and x P∗ i corresponds to the C α coordinate of this residue. Details of the graph construction can be found in Appendix.A.6.

Overview. Our goal is to train a model f that excels in both site-specific docking and blind docking scenarios of rigid molecular docking, wherein the protein structure is fixed and only the ligand's flexibility is considered.

As depicted in Fig. 1, DeltaDock comprises two modules: a pocket-ligand alignment module CPLA responsible for selecting binding pocket from a pocket candidate set, and a bi-level iterative refinement module Bi-EGMN dedicated to executing site-specific docking given the binding pockets. This design allows DeltaDock to handle both blind docking and site-specific docking seamlessly. In the subsequent part of this section, we will elaborate on these two modules respectively.

3.2 Contrastive Pocket-ligand Alignment

CPLA treats the pocket prediction task as a pocket-ligand alignment problem. We employ a list of well-established ligand-free pocket prediction methods to generate candidate pocket sets, and then map these pockets and the target ligand into the same embedding space. The correct pocket embedding is expected to have higher similarity with ligand embedding than other pockets.

3.2.1 Data Preprocessing

The initial step of this module involves using RDKit [27] to generate a 3D conformer of the input ligand, as depicted in Fig. 1. Binding site prediction models including P2Rank and DSDP are adopted to extract druggable binding sites, and the binding sites predicted by these different methods are combined to form a set of candidate binding sites, denoted as S = { ς 1 , ς 2 , ... } , where ς i represents the geometric center of i -th binding site. For CPLA, the protein pocket ρ i is defined as the residues within 15.0 Å to ς i .

3.2.2 Ligand and Pocket Encoders

To map the ligand and pockets into the embedding space, the ligand encoder Attentive-FP (AFP) [28] and protein encoder Geometric Vector Perceptron (GVP) [29] are employed. These encoders first extract informative ligand node and protein node representations, and the feature extraction process can be formally expressed as:

where H L is the ligand embedding matrix of shape |V L |× d and H P∗ is the protein residue embedding matrix of shape |V P∗ | × d . The ligand representations m L and pocket representations m ρ i are then obtained by pooling ligand nodes embedding and pocket nodes embedding:

where V ρ i is the protein node set of i -th pocket ρ , and the pooling operation is sum pooling. For the pocket encoder, we input the entire protein residue graph G P∗ rather than just the protein pocket residue graph, to incorporate global protein information into the pocket representation.

3.2.3 Contrastive Embdding Alignment

With ligand representation m L and pocket representation m ρ i in hand, we calculate the cosine similarity score:

For the candidate pockets S = { ς 1 , ς 2 , ... } , the similarity score s + between the target pocket and the ligand is expected to be higher than others. Thus, we propose the contrastive learning objective:

where τ is the temperature paramter. For blind docking, the pocket with the highest similarity score with the ligand is selected for the next docking step.

3.3 Bi-level Iterative Refinement

With a binding site ς predefined by the user or selected by CPLA, we design the bi-level iterative refinement module Bi-EGMN to predict binding pose within this pocket (Fig. 1).

3.3.1 Inital Structure Sampling

For an iterative refinement module, an initial structure is needed as a starting point. Previous work on molecular 3D conformer generation [17] demonstrates the importance of a good initial structure. Therefore, Bi-EGMN adopts a rapid GPU-accelerated sampling method proposed by Huang et al. [23] to sample a high-quality initial X L . In this work, the search steps number and the search copy number are set to 40 and 384, respectively. Details about the search box setting can be found in the Appendix.B.3.1.

3.3.2 Structure Refinement

With input initial structure X L , we iteratively update it to improve its accuracy. As discussed in Sec.1, the modeling of an entire binding pocket structure is crucial for the success of the process. Current methods either ignore the atom-level structure or model the full-atom pocket structure directly. The latter approach can significantly elevate the computational resource demand, particularly when dealing with large pockets. To overcome these challenges and maintain high docking accuracy and efficiency, we propose a bi-level strategy in this work. In the following sections, we first present the details of the bi-level strategy. Subsequently, we discuss the Bi-EGMN layer, which is used to perform refinement, as depicted in Fig. 1.

Bi-level strategy. The first refinement level is the residue level, where the protein residues within a 40.0 Å cubic region centered at the geometric centers of ligands are considered as pocket ρ . Previous work demonstrates such a range is large enough to cover the binding pocket [30]. In this context, as the full-atom structure of proteins is not considered, the pocket residue graph G ρ ∗ is adapted. The second level is the atomic level, where we set the ligand structures refined through T rounds of residue level refinement as the reference structure. In this level, protein atoms within a 6 . 0 Å radius of the ligand atoms are considered to construct pocket atomic graph G ρ for modeling the fine-grained interaction. The ligand coordinates X a, L output by the last layer of atomic level refinement correspond to the final predicted structure ˆ X L .

Bi-EGMN Layer . The bi-level E(3)-equivariant graph matching network (Bi-EGMN) layer is the model designed to calculate the protein-ligand interaction and refine the structures. More specifically, this layer adheres to the message-passing paradigm [31] and consists of four functions: intra-message function, inter-message function, aggregate function, and update function.

The intra-message function works to extract messages m i,j and ˆ m i,j between a node i and its neighbor nodes j from the same molecule graph. m i,j is later used for the updating of node features and ˆ m i,j for the updating node coordinates. ∀ ( i, j ) ∈ E P ∪ E L , this function can be formally written as :

where d ( l ) i,j is the relative distance between node i and node j , and φ is a MLP.

The inter-message function works to extract messages µ i,j and ˆ µ i,j between a node i and its neighbor nodes j from the other molecule graphs. Formally, ∀ i ∈ V P , j ∈ V L or i ∈ V L , j ∈ V P :

After extracting inter-message and intra-message, the aggregation function aggregates the neighbor messages of the node i . ∀ i ∈ V P ∪ V L :

where N ( i ) is the neighbor of node i in the same graph, and N ( l ) ∗ ( i ) is the set of nodes associated with node i in the other graph.

Finally, the update function updates the position and features of each node:

where β and η are feature skip connection weight and coordinates skip connection weight, respectively. Through such a message-passing paradigm, our Bi-EGMN layers make to update coordinates iteratively.

3.3.3 Fast Structure Correction

Lastly, as Bi-EGMN updates structures by modifying the coordinates rather than the torsional angles, as is done in methods like DiffDock [9] and other sampling-based methods, it is crucial to ensure the plausibility of bond lengths and bond angles of the updated structure ˆ X L . Therefore, fast structure correction steps, torsion alignment, and SMINA-based energy minimization are designed.

Torsion Alignment. We employ a rapid torsion alignment for the updated structure. The target of this alignment is to align the input structure X L with the updated structures ˆ X L by rotating its torsional bonds. Formally, let ( b i , c i ) denote a i -th rotatable bond, where b i and c i are the starting and ending atoms of the bond, respectively. We randomly select a neighboring atom a i of b i and a neighbor atom d i of c i to calculate the dihedral angle ˆ δ i = ∠ ( a i b i c i , b i c i d i ) based on updated structure coordinates ˆ X L . Subsequently, we rotate the rotatable bond ( b i , c i ) of input structures to match its dihedral angle δ i the same as ˆ δ i . This simple operation can be implemented efficiently using RDKit. After all rotatable bonds have been rotated, we align the rotated input structure to the updated structures to obtain the torsionally aligned structure ˆ X LT . This process ensures the plausibility of bond lengths and bond angles in the torsionally aligned structure ˆ X LT .

Energy Minimization. To further enhance the reliability of DeltaDock, we implement an energy minimization on the torsionally aligned structure ˆ X LT , when an inter-molecular steric clash between the protein and ligand is detected. This energy minimization is conducted using SMINA [19], as it is a highly efficient tool for this process compared with specialized energy minimization tool OpenMM [32] (details see Appendix.A.5). The output structure of this process is ˆ X L ′ .

3.4 Training and Inference

3.4.1 CPLA

The training object L is a contrastive object defined before (Eq. 4). For a protein and its candidate pockets set S = { ς 1 , ς 2 , ... } , the positive pair is the target pocket-ligand pair and the negative pairs are other pocket-ligand pairs. The pocket-ligand pairs across different proteins are not used. When training, we calculate the minimum center distance ( DCC min ) between all candidate pockets and the ligand. If DCC min ≤ 5 . 0 Å, we add the ligand center into S to assert the existence of positive pairs for every protein (details see Appendix.B.3.1).

1 The time of consumption is denoted with * if it only consumes CPU.

2 All results of baselines are taken from [11] for fair comparison.

3.4.2 Bi-EGMN

We design a physics-informed loss function for the Bi-EGMN module for training. The coordinates X a, L and X r, L output by the last layer of atomic level and residue level are both employed in the computation of this loss. Formally, the loss function can be expressed as follows:

where λ are weight hyper-parameters. Among the four components, inter-distance map loss L inter is responsible for the RMSD accuracy. Other three items, namely intra-distance map loss L intra , vdw constraint loss L vdw , and bound matrix constraint loss L bound are employed for physical validity. When training and inferencing, we follow previous work [33] and employ the recycling strategy (details see Appendix.B.3.2).

4 Experiments

4.1 Settings

Dataset. We conduct experimetns on PDBbind [34] v2020 and PoseBusters [16] datasets in this work. Our model is trained on the PDBbind dataset, where the training, validation, and testing set are constructed based on the time split strategy used in previous work [11]. PoseBusters, which contains 428 carefully selected data released from 1 January 2021 to 30 May 2023, is directly adopted to evaluate the ability to predict physically valid poses.

Evaluation. Root-mean-square-deviation (RMSD) and centroid distance (CD) are used to evaluate the docking accuracy of different docking methods, and the PoseBusters [16] test suite is employed to evaluate the performance of predicting physically valid poses. Additionally, as pocket prediction plays an important role in our framework, the distance between the center of the predicted pocket and the center of the ground-truth ligand structure (DCC), and the volume coverage rate (VCR) are employed to evaluate the pocket prediction accuracy (details in Appendix.B).

4.2 Overall Performance on the PDBbind

We first assess the comprehensive performance of DeltaDock on the PDBbind dataset, encompassing both blind docking and site-specific docking settings.

a)

b)

60

57.3%

56.5%

60%-

RMSD≤ 2A

58%

57%6%

50

43.5%

45.0%

46.0%

51.6%

45%

43%

50%50%

47%46%

43%

Rate

40

36.0%

40 %-

38%

Success

30

Percentage

23.4%

25%

20-

20 %-

15%

14%

10

5.5%

2.6%

2.6%

0

0%

0%

0%

EquiBind

TANKBbind

DiffDock

FABind+

VINA

SMINA

dasa

DeltaDock

EquiBind

TANKBind

DiffDock

FABind+

VINA

SMINA

dasa

DeltaDock

DeltaDock-SC

DeltaDock-SC

4.2.1 Blind Docking

As demonstrated in Table.1, DeltaDock outperforms all baseline methods. Specifically, DeltaDock achieves a remarkable success rate of 47.4% (where RMSD < 2.0 Å), surpassing the previous SOTA GDL method, DiffDock, which has a success rate of 36.0%. Recent GPU-accelerated docking methods have also made significant progress in blind docking. However, when compared to DSDP, which is the top-performing sampling-based method in the PDBbind test set, DeltaDock still exhibits superior performance across all metrics. Notably, as elucidated in Section 3.3, DeltaDock employs the same sampling algorithm as DSDP for generating the initial structure. Yet, our framework allows DeltaDock to significantly outperform DSDP.

Beyond accuracy, efficiency is a critical performance measure for molecular docking methods. As indicated in Table 1, DeltaDock maintains a competitive level of efficiency, despite the inclusion of an energy minimization operation to enhance accuracy and reliability. Molecular docking methods invariably face a trade-off between efficiency and accuracy. However, the data presented in Table 1 suggest that DeltaDock could serve as a viable tool for practical applications, balancing these two crucial aspects effectively.

4.2.2 Site-specific Docking

Most existing GDL methods, such as DiffDock and EquiBind, are primarily designed for blind docking scenarios and are not inherently suited for site-specific docking tasks. However, DeltaDock seamlessly integrates blind docking and site-specific docking settings. In this context, the pocket is directly provided, eliminating the need for pocket selection via CPLA. The performance of DeltaDock in site-specific docking is illustrated in Fig.2. When supplied with predefined binding sites, traditional sampling methods exhibit a significant improvement in results. For instance, the docking success rate of VINA escalates from 10.3% to 45.0%. Despite this enhancement, DeltaDock consistently surpasses all baselines. Previous research suggested that while GDL docking methods excel at pocket searching, traditional methods tend to outperform GDL models in site-specific docking tasks [35]. However, as evidenced by the results presented in Table.1 and Fig.2, DeltaDock exhibits superior performance in both blind and site-specific docking scenarios, demonstrating its versatility and robustness in handling diverse docking settings.

4.3 Evaluation of Pose Validity

We further investigate DeltaDock's ability to predict physically valid structures by employing the PoseBusters test suite, as designed by Buttenschoen et al. [16]. In addition to the RMSD between predicted and ground-truth poses, the test suite incorporates 18 checks, encompassing chemical validity and consistency, intramolecular validity, and intermolecular validity. When physical validity

a)

b)

1.0

1.0

RMSD of updated poses

10

PDBbind Test Set

Success Rate

0.8

CPLA-Top-1 Pocket

P2Rank-Top-1 Pocket

DSDP-Top-1 Pocket

Success Rate

0.8

口

CPLA-Top-1 Pocket

P2Rank-Top-1 Pocket

DSDP-Top-1 Pocket

8

0.6

SiteMap-Top-1 Pocket

Fpocket-Top-1 Pocket

0.6

SiteMap-Top-1 Pocket

Fpocket-Top-1 Pocket

6

0.4

DoGSite-Top-1 Pocket

0.4

DoGSite-Top-1 Pocket

4

0.2

0.2

2

0.0

0

1

2

DCC

3

4

0.0

0

5

Cube sidelength

10

15

20

25

30

2

RMSD of initial poses

4

6

8

10

1.0

1.0

RMSD of updated poses

10

CPLA-Top-1 Pocket

CPLA-Top-1 Pocket

Success Rate

0.8

口

P2Rank-Top-1 Pocket

DSDP-Top-1 Pocket

Success Rate

0.8

口

=

P2Rank-Top-1 Pocket

DSDP-Top-1 Pocket

8

PoseBuster

0.6

Fpocket-Top-1 Pocket

SiteMap-Top-1 Pocket

0.6

Fpocket-Top-1 Pocket

SiteMap-Top-1 Pocket

6

DoGSite-Top-1 Pocket

DoGSite-Top-1 Pocket

0.4

0.4

4

0.2

0.2

2

0.0

0

1

2

DCC

3

4

0.0

0

5

Cube side length

10

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20

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30

0

2

RMSD of initial poses

4

6

8

10

is considered, the docking success rates of traditional sampling methods remain stable, while the performance of previous geometric deep learning methods significantly declines, especially for TANKBind, DeepDock, and Uni-Mol. The DeltaDock-SC variant, even without the application of the fast structure correction step, shows significant improvement over previous methods. These results substantiate DeltaDock's capacity to predict physically valid structures, thereby affirming its reliability for practical applications.

4.4 Further Analysis

4.4.1 Pocket-ligand Alignment and Iterative Refinement

Beyond the overall docking performance, the pocket-ligand alignment and iterative refinement results are explored (Fig. 3). As depicted in the figure, CPLA predicts significantly more accurate pockets than other methods and Bi-EGMN can diminish the discrepancy between ground-truth structures and input structures. Generally, the PDBbind test set poses a more significant challenge to Bi-EGMN than the PoseBusters dataset. And for CPLA, PoseBusters dataset is more challenging otherwise. The consistent good performance on the two datasets demonstrates the effectiveness and generalization capacity of CPLA and Bi-EGMN.

4.4.2 Ablation Studies

In this section, ablation studies are conducted to assess the contributions of different components. We first ablate the whole CPLA or Bi-EGMN, and then the residue-level or the atom-level in Bi-EGMN (see Appendix. B.4 for implement details). As illustrated in Table 2, it becomes clear that each component, encompassing CPLA and the bi-level strategy in Bi-EGMN, plays a significant role in enhancing the overall performance of DeltaDock. Due to the space limitation, a full ablation study can be found in Appendix. C.3.

5 Conclusion

In this work, we proposed DeltaDock, a unified framework for accurate, efficient, and physically reliable molecular docking. DeltaDock was a two-stage docking framework, consisting of pocket prediction and site-specific docking. We innovatively reframed the pocket prediction task as a pocketligand alignment problem and then followed a hybrid strategy to jointly utilize both GDL and physicsinformed traditional algorithms for site-specific docking. Comprehensive experiments demonstrated the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieved a 31% relative improvement over the docking success rate compared with the previous state-of-

the-art GDL model. We hope this work will further facilitate the broad application and continued development of the molecular docking framework.

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A More Detailed Descriptions

A.1 Dataset Preprocessing

We follow the time split strategy used in previous work [8, 30, 11] to split the dataset to construct the train, validation, and test set. All compounds discovered in or after 2019 are in the test and validation sets, and only those found before 2019 are in the training set. The training set, validation set, and test set have 17,299, 968, and 363 complexes, respectively. The overall performance of docking methods is evaluated on the time spit test set following previous works. In this work, we only select the protein chains within 10 Å to the ligand structure.

A.2 Dataset Statistics

Proteins are inherently macromolecules composed of multiple chains, with each chain potentially containing hundreds or even thousands of residues [36, 37, 38]. In Table.3, we statistically analyze the PDBbind time-split test set and count atom numbers in proteins. Notably, it can be observed that the number of atoms escalates substantially as the cutoff value increases.

A.3 Example of Large Pocket

Large pockets that consist of several sub-pockets generally exist. For example, the main protease of SARS-CoV-2 (Fig. 4).

A.4 Analysis of Existing Pocket Prediction Methods

As depicted in Fig.5, existing pocket prediction methods generally achieve a hit rate of approximately 70%-80%, where the distance between the predicted pocket center and ligand center (DCC) is less than 5.0 Å. Notably, when leveraging combined predictions from multiple methods, the hit rate significantly increases to nearly 95%. Motivated by this observation, DeltaDock begins with a ready-to-dock ligand and a candidate pocket set derived from a suite of existing pocket prediction models.

We further statistics how many pockets these methods predict in Fig. 6. We observe that Fpocket [24], and DoGSite3 [39] output much more pockets than DSDP [23], P2Rank [25], and SiteMap [40, 41].

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Combining information from Fig. 6 and Fig.5, it is evident that the pockets predicted by DSDP and P2rank are highly druggable. Other methods, in contrast, tend to predict many non-druggable pockets.

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A.5 Efficiency Comparison between SMINA and OpenMM

For AI-based structure prediction methods, including AlphaFold2 [33], it is common practice to employ energy minimization methods for post-processing to ensure the physical validity of the predicted structures. While specialized methods like OpenMM are available for energy minimization, we opted not to use them due to computational efficiency considerations. Specifically, we found that SMINA, which is typically known as a docking method, requires only approximately 0.4 seconds for energy minimization. This is significantly faster than methods like OpenMM, which can take several minutes to tens of minutes per protein-ligand pair, as illustrated in the Table. 4 below.

For molecular docking, efficiency is crucial, and specialized methods such as OpenMM can be excessively time-consuming. What's more, it is important to note that SMINA, although generally regarded as a docking method, is not employed for docking in our workflow but rather utilized in its minimization mode for energy minimization.

A.6 Graph Construction

Ligand Graph. The input ligand L is first represented as a ligand graph G L = ( V L , E L ) , where V L is the node set and node i represents the i -th atom in the ligand. In this work, RdKit [27] is employed to generate a 3D initial conformer of the input ligand. Each node v L i is also associated with an atom coordinate x L i retrieved from the individual ligand structure P and an atom feature vector h L i . The edge set E L is constructed according to the spatial distances among atoms. More formally, the edge set is defined to be:

where cut L is a distance threshold, and each edge ( i, j ) ∈ E L is associated with an edge feature vector e L ij . The node and edge features are obtained by RDKit [27] in the CPLA. And in the Bi-EGMN, they are achieved by OpenBabel [42]

Protein Atomic Graph. The protein atomic graph G P is constructed in the same way as the ligand graph.

Protein Residue Graph. For protein residue Graph G P∗ = ( V P∗ , E P∗ ) , V P∗ is the node set and the node i represents the i -th residue in the protein. Each node v P∗ i is also associated with an C α coordinate of the i -th residue x P∗ i retrieved from the individual protein structure and a residue feature vector h P∗ i . The edge set E P∗ is constructed according to the spatial distances among atoms. More formally, the edge set is defined to be:

where cut P∗ is a distance threshold, and each edge ( i, j ) ∈ E P∗ is associated with an edge feature vector e P∗ ij . The edge features are obtained following [9]. As for the node features, they are extracted from the protein language model ESM2-3B [43] in CPLA. While in Bi-EGMN, they are obtained following [8].

B More Detailed Experimental Settings

B.1 Baselines

For molecular docking, GDL methods, EquiBind [8], TANKBind [30], DiffDock [9], DeepDock [44], Uni-Mol [14], FABind [11], and FABind+ [45] and traditional sampling methods, VINA [18], SMINA [19], and DSDP [23] are used as baselines. According to their performance on both PDBbind and Posebusters, DiffDock is the previous SOTA GDL docking method, and we thus mainly focus on the comparison with DiffDock. For pocket prediction, DSDP, P2Rank [25], Fpocket [24], SiteMap [40, 41], and DoGSite3 [39] are compared.

B.2 Evaluation Metric

For blind docking and site-specific docking, RMSD and centroid distance are used to evaluate different methods, the formal definitions of these two metrics are:

where x L i is the ground truth coordinate of i -th ligand atom and ˆ x L ′ i is the predicted coordinates. In alignment with previous studies [15, 9], for blind docking, the RMSD is directly computed. However, in the case of site-specific docking, the RMSD is calculated utilizing the spyrmsd [46].

For pocket prediction, the DCC metric is defined as:

where ˆ ς is the predicted pocket center. As for the VCR metric [23], we calculate the cube side length of a cube box centered on the pocket that can cover the whole ligand structure.

B.3 Training and inference

Our models are trained using NVIDIA A100-PCIE-40GB GPUs. Training the CPLA on a single GPU takes approximately 2 hours, while the Bi-EGMN requires about 48 hours on 4 GPUs. To determine the hyperparameters, we performed a grid search, as outlined in Table 5 and Table 6.

B.3.1 CPLA

Basic Settings. The model was trained employing the Adam optimizer [47] with an initial learning rate of 0 . 0003 and an L 2 regularization factor of 10 -6 . The learning rate was scaled down by 0.6 if no drop in training loss was observed for 10 consecutive epochs. The number of training epochs was set to 20 with an early stopping rule of 10 epochs if no improvement in the validation performance was observed.

Candidate Pockets Generation. For CPLA, we consider two methods to generate candidate pockets: DSDP, and P2Rank. These methods were selected over others, such as SiteMap. Initially, we intended to incorporate all available methods to construct the candidate pockets. However, the results were unsatisfactory. This could be attributed to the issue of hard negative samples. CPLA employs contrastive learning, where the quality of hard negative sample selection directly impacts the training performance. In this context, hard negative samples represent highly druggable pockets that are not the target pocket. As illustrated in Fig. 6 and Fig.5, the pockets predicted by DSDP and P2rank are highly druggable. In contrast, other methods tend to predict non-druggable pockets. The result in Table. 7 demonstrates that introducing FPocket impairs the training quality. Consequently, we opted to solely use DSDP and P2rank.

Pocket Augmentation. Given a candidate pockets set S = { ς 1 , ς 2 , ... } , we establish a maximum pocket number, N max , to construct negative pockets for data augmentation. If | S | > = N max , we select the topN max pockets in the sort of DSDP, P2Rank accordingly. If | S | < N max , we randomly select ( N max - | S | ) C α atoms that are more than 20.0 Å from the ligand geometric center to construct negative pocket centers. This data augmentation is only applied in the training phase.

Ligand Conformation Augmentation. During the CPLA training, we further considered the issue of the native binding mode. As the native binding mode (i.e., the co-crystal structure) of a given molecule is unknown in practical scenarios, we aim to train a pose-robust CPLA model. To achieve this, we adjusted the rotatable bond angles of the co-crystal molecule structure in each epoch during training. Therefore, the molecule poses in each epoch are perturbed and different.

Other Training Object. We have considered using cross-protein loss for training, where the ground truth pockets and ligands from the same protein-ligand pairs are considered positive samples, and those from different protein-ligand pairs are treated as negative samples. Although this loss has been utilized in previous work for virtual screening [48], it was found to be unsuitable for our model.

B.3.2 Bi-EGMN

Basic Settings. The Adam optimizer [47], characterized by an initial learning rate of 10 -3 and an L 2 regularization factor of 10 -6 , is employed for training Bi-EGMN. The learning rate was scaled down by 0.6 if no drop in training loss was observed for 10 consecutive epochs. The number of training epochs was set to 1000 with an early stopping rule of 40 epochs if no improvement in the validation performance was observed.

Training Object. The loss function can be written as:

As introduced before, the inter-distance map loss L inter is responsible for the RMSD accuracy. Other three items, namely intra-distance map loss L intra , vdW constraint loss L vdw , and bound matrix constraint loss L bound are employed for physical validity.

The two distance map losses can be formally expressed as:

where predicted distance d pred ij = || x pred i -x pred j || and ground-truth distance d gt ij = || x gt i -x gt j || between node i and j are calculated based on node coordinates.

The other two physics-informed losses can be formally expressed as:

where the vdW distance d vdw ij = 0 . 75( r vdw i + r vdw j ) is calculated based on node van der Waals radii r vdw . As for the lower bound distance d bd,low ij and upper bound distance d bd,up ij , they are determined based on the bound matrix generated by RDKit [49] following [16].

Initial Poses Augmentation. In the training phase of the Bi-EGMN, initial pose augmentation is employed. The initial poses utilized for training are sampled based on the ground truth pocket. An adaptive box is defined through a two-step process: (1) the minimum and maximum of the x, y, and z coordinates of the ligand are extended by 4 Å each; (2) if the box size is less than 22.5 Å after the first step, it is further extended to 22.5 Å. During the inference phase, however, the box size is fixed at 30.0 Å, deviating from the adaptive strategy employed during training. For each epoch during training, a pose is randomly selected. This pose augmentation strategy significantly amplifies the diversity of the input. As depicted in Fig.7, the sampled poses can nearly encompass the entire pocket cavity.

Recycling. During both training and inferencing, the recycling strategy is adopted. For training, we randomly recycle the iterative refinement process 1-3 times, and only the last cycle is used to compute the gradient. For inferencing, the recycle number is fixed to 4.

B.4 Ablation Studies Settings

w/o CPLA : pockets predicted by DSDP are employed to perform the following predictions.

w/o Bi-EGMN : the sampled structures are directly employed as final structures to calculate metrics.

w/o Residue Level : the residue level is removed from Bi-EGMN.

w/o Atom Level : the atom level is removed from Bi-EGMN.

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C More Experimental Results

C.1 Binding Pocket Prediction

C.1.1 Overall Performance on PDBbind

In addition to the overall performance presented in Fig.3, we offer a more detailed analysis in Fig.8. As can be discerned from the figure, the top-1 pockets predicted by CPLA significantly outperform those predicted by other baseline methods. Furthermore, when considering the top-2 pockets, the accuracy of pocket prediction is on par with the cumulative performance of all pockets predicted by other methods.

C.1.2 Results of Different Candidate Pockets

In the current framework, only DSDP and P2Rank are selected to generate candidate pockets. The motivation and analysis for this operation have been discussed before. To support this selection, we further present the experimental results of employing different candidate pockets to train CPLA in Table. 7. These results indicate that only selecting DSDP and P2Rank yields to best performance.

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C.1.3 Influence of Ligand Conformations

When training CPLA, we employ a conformation augmentation strategy to train a pose-robust CPLA model. The provided Table. 8 illustrates CPLA's performance when presented with both a co-crystal ligand structure and an RDKit-generated ligand structure, showcasing the model's resilience to ligand poses and the effectiveness of our strategy.

C.1.4 Comparison with FABind

Previous pocket prediction methods, such as DSDP and P2RANK, are ligand-independent. Their goal is to predict all possible binding sites. However, in molecular docking, the goal is to predict targeted binding sites. There are now methods that, like CPLA, are ligand-dependent, such as FABind. To further demonstrate the effectiveness of CPLA, a comparison on pocket prediction accuracy is conducted between FABind and CPLA as shown in Table. 9. Obviously, our model achieves a significant advantage.

C.2 Blind Docking Performance on PoseBusters

Due to the space limitation, only site-specific docking performance on PoseBusters has been presented before. In Fig. 9, we provide the blind docking performance on PoseBusters. We observed that DeltaDock achieves a docking success rate of 48.8% even when considering the physical validity.

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C.3 Detailed Ablation Studies

Comprehensive ablation experiments were performed within two distinct contexts: blind docking utilizing the PDBbind dataset to assess the impact on RMSD metrics, and site-specific docking employing the PoseBusters dataset to evaluate the influence on the physical plausibility of the predicted binding poses.

C.3.1 Ablation Studies On PDBbind

Table.10 presents more detailed ablation studies on PDBbind, including the removal of recycling, training loss components, structure correction, and structure sampling initialization. From the table, we observe that: (1) each component contributes to the good RMSD performance of our DeltaDock. (2) The training loss items and structure correction step employed for physical validity tend to decrease the RMSD performance. (3) The structure sampling algorithm used for initialization is especially important for good RMSD performance. (4) When we train DeltaDock like previous docking methods, removing the loss items and structure correction step for physical plausibility, DeltaDock still achieves a competitive performance and outperforms all other GDL methods significantly on the test unseen set even without the using of structure sampling algorithm. These results demonstrate the effectiveness of DeltaDock.

C.3.2 Ablation Studies On PoseBusters

Fig. 10 and Fig. 11 present ablation studies on PoseBusters to explore the effect of physics-informed training items and structure correction step. From the figures, we can see that: (1) the physicsinformed training items and structure correction step contribute to the good physical validity of DeltaDock. (2) Among the physics-informed training items, L intra is especially important for the GDL model to predict valid structures without post-processing. These results demonstrate the effectiveness of DeltaDock.

DeltaDock

DeltaDock

100%-

4280-182

DeltaDockw/o torsion align

100% -

4280-182

DeltaDockw/o energy minimization

0

0

80%-

80%-

60%-

0

0

0

0

0

0

0.

-4

0

0

-1

-

0

0

0

0

0

0241

60%

0

0

0

0

0

0

0.

-4

0

0

-1

0

.....

0241

-1780

0

0

-20-1

-2

0

0

0

-1

0

0

0

0

0

0

225

-1890

0

0

0

0

0

0

-8

0

0

E-

40%-

40%-

-75

0

0

0

0

0

153

20%-

20%-

0%

0%

All predictions

RMSD >

2A

ring

Al predictions

aded

RMSD >

2A

Energy

Input cannot be loa

nput cannet be

Mr

tMif

tMif

DeltaDock-SC

DeltaDock

100%-

4280-180

DeltaDock-SC w/o Lvdw

100%-

4280-182

DeltaDock w/o Lvdw

0

80%

80%

60%-

000-18

-1-2-1000-2-38

60%-

-177000

-12

00000186

-1860

0

0

237

40%

6810000066-

40%

20%-

20%

0%

0%

DeltaDock-SC

DeltaDock

100%-

428

30-180

0

DeltaDock-SC w/oLintra

100%-

4280-182

。

DeltaDock w/o Lintra

80%

80%-

60%

00

-1-2-1000-2-38

60%-

40%-

-55

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。。。。

0186

40%-

-1810000000-200

20%

20%-

136

-150

-1

0%

0%

DeltaDock-SC

DeltaDock

100%

4280-180

DeltaDock-SC w/o Lbound

100%-

4280-182

DeltaDock w/o Lbound

80%-

80%-

60%-

-186 0

000-18

0

0

7

-160

-1-2-1000-2-38

30-302

00000186

60%-

-1960

0

0

40%-

2900100188

40%-

20%-

20%-

0%

0%

1 The time of consumption is denoted with * if it corresponds to GPU time; in the absence of this symbol, the time pertains to CPU time.

1 The time of consumption is denoted with * if it corresponds to GPU time; in the absence of this symbol, the time pertains to CPU time.

C.3.3 Detailed Tables of Performance on PDBbind

In the main manuscript, we have opted to present a concise table Table. 1 with key metrics due to the space constraint. Here, we present the detailed tables Table. 11 and Table. 12, which encompasses all metrics on PDBbind.

D Broader Impacts and Limitations

D.1 Broader Impacts

The development and maintenance of computational infrastructure for AI-assisted molecular docking represent a significant allocation of resources. Inefficient allocation or underutilization of these resources can potentially result in resource wastage.

D.2 Limitations

One disappointing limitation is the reliance on external tools, such as SMINA for post-processing and the structure sampling algorithm for structure initialization. Although DeltaDock still achieves the best performance among GDL methods on the test unseen time split set without these tools, the overall performance degrades. Indeed, due to the limited training data, it's quite difficult to accomplish accurate, efficient, and physically reliable docking without any external tools. In the

future, we will try to overcome this limitation by exploring pre-training strategies on large-scale datasets generated by docking methods or recently developed AlphaFold3 [50].

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Peer reviews:
comments: SUMMARY:
This paper proposed a model named DeltaDock for molecular docking. DeltaDock integrates protein pocket prediction and protein-ligand binding refinement. The pocket prediction is modeled as a pocket-ligand alignment problem with candidates of pockets given by other pocket prediction methods. The docking is formulated as a refinement problem given the initial docking conformation. Comparison with computational chemistry docking tools and learning-based methods show the advantages of DeltaDock. DeltaDock runs much faster than most of the baselines.

STRENGTHS:
1. The model is faster than the diffusion-based method DiffDock.
2. The experimental results show that DeltaDock has a good performance in blind docking.

WEAKNESSES:
1. The testing set is not clearly described. Training and test proteins should have a maximum sequence identity of 40% to test the generalization.  
2. in figure2, the compared methods are not consistent. For example, vina is in Figure 2 (a) but not in Figure 2 (b). This may be sometimes misleading.  
3. in Line 60, "a GPU-accelerated pose sampling algorithm generating high-quality initial structure" is just DSDP, which limits the novelty of the paper.  
4. what if you combine the pocket prediction of DeltaDock and autodock vina? Whether the improvement is due to the pocket prediction or the refinement is not clear.

QUESTIONS:
what is the distance threshold used for graph construction?

LIMITATIONS:
Yes
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript introduces DeltaDock, a novel two-stage framework for molecular docking. Similar to previous works that use geometric deep learning methods, this work also takes the geometric dl network to do the modeling and the prediction is based on a regression problem. The main contribution is a contrastive based learning method for pocket prediction and also a bi-level update of the ligand pose prediction. The experiments are conducted on common datasets and also many studies are performed.\n\nSTRENGTHS:\n1.\tThe overall framework makes sense and is valid and useful for blind molecular docking.\n2.\tThe proposed contrastive learning approach for pocket prediction and the bi-level refinement module are novel contributions to the field. CPLA, in particular, reframes pocket prediction as a selection problem from a candidate set.\n3.\tLots of small tricks designed in the framework are beneficial to hold the correct structure and also help the final prediction. The detailed ablation studies, generalization analysis, and assessment of pose validity highlight the contribution of each component.\n4.\tThe experiments results are good compared to previous works.\n\nWEAKNESSES:\n1.\tThough the process and the framework are reasonable, the overview of the work is quite similar to FABind, which implements a pocket prediction and a docking prediction molecules, The authors should put more comparison and describe more differences between these two works.\n2.\tAs the prediction module takes other external methods/tools, this framework is not a fully end-to-end one, though this is not a thing that we would purse must, I would like to hear my thoughts on this point. Why not do in an end-to-end way? The external tools indeed cost more and slow down the docking process.\n3.\tThe manuscript focuses on rigid protein docking, neglecting the inherent flexibility of protein side chains. Can the method get rid of the assumption that the protein side is rigid, modeling the side chain flexibility like previous works? [1, 2]\n\n[1] Qiao, Zhuoran, et al. \"State-specific protein–ligand complex structure prediction with a multiscale deep generative model.\" Nature Machine Intelligence 6.2 (2024): 195-208.\n\n[2] Plainer, Michael, et al. \"Diffdock-pocket: Diffusion for pocket-level docking with sidechain flexibility.\" (2023).\n\nQUESTIONS:\nPlease refer to Weakness Section\n\nLIMITATIONS:\nPlease refer to Weakness Section", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nA new method for molecular docking based on neural networks, called DeltaDock, is introduced. DeltaDock uses a two-step procedure. The first step is finding the binding pocket for a given ligand, which is implemented by aligning the molecular structure with the pocket embedding. The alignment is conducted with the use of contrastive learning. The second step is the placement of the ligand in the binding pocket, which is defined as a regression task on atom positions. This step includes predicting interactions on a coarse and fine level of protein representation. A fast pose correction is implemented using torsion alignment and SMINA-based energy minimization. The experimental section shows that the proposed method outperforms other state-of-the-art methods in terms of the RMSD of generated poses. Moreover, these structures are more realistic than those produced by other deep learning approaches in terms of the PoseBuster filters.\n\nSTRENGTHS:\nOriginality:\n- The idea of pocket alignment using contrastive learning is interesting and novel in this context. This approach is usually used to predict drug-target interactions. It is interesting to see it applied to find binding pockets based on the ligand input.\n- The torsion alignment proposed in this paper is a new and effective method of correcting conformations produced by regression models.\n\nQuality:\n- The experimental section contains results on two data splits and compares both classical and neural-network-based molecular docking models, which places the DeltaDock among the state-of-the-art methods.\n- The average time of inference is provided for each method, which demonstrates the difference in execution time between DeltaDock and other methods.\n\nClarity:\n- Overall, the paper is written clearly and is easy to follow.\n\nSignificance:\n- The experiments show the proposed method's strong performance, outperforming other approaches in terms of pose quality and alignment with the ground-truth pose.\n- The full docking protocol includes some constraints that ensure there are no clashes or torsional strain. This is important due to the recent criticism of deep-learning docking methods.\n- At the same time, DeltaDock is faster than diffusion methods like DiffDock, which can further accelerate virtual screening.\n\nWEAKNESSES:\nOriginality:\n- The Bi-EGMN is based on a few architectural decisions that may have influenced the model's strong performance. For example, in Equations 7 and 8, the messages are weighted by the interatomic distances. In Equations 9 and 10, skip connections are used. Have you compared this architecture with one that uses other message weights or does not use skip connections? It would be interesting to see it in an ablation study.\n- FABind+ should also be described in the related work as it follows a similar approach, where first the binding pocket is predicted, and then the molecule is docked inside this binding pocket. Interestingly, the size of the binding pocket is also predicted in this improved version of FABind, which speaks to the claim that current GDL methods have “difficulties in handling large binding pockets.” Also, FABind+ shows very promising results in their paper. Now, the code of this method has been published, so it would be interesting to see this model in the comparison if possible in such a short discussion period.\n\nQuality:\n- I am wondering why the average (or median) RMSD is not provided in Table 1. Other papers, including FABind and EquiBind also provided percentiles.\n\nClarity:\n- Figure 1b could be improved to better depict the coarse level and atom level pocket representation. I am unsure if the model block shown in between refinement steps helps in the comprehension of this figure.\n- The sentence in line 31 is imprecise. These methods use binding pockets in some sense because they encode the whole protein and use it to condition the generative or predictive model.\n\nQUESTIONS:\n1. Is it possible to generate diverse poses with DeltaDock, e.g. by sampling different initial conformations? How diverse can these poses be, given that the networks are E(3)-equivariant?\n2. Do you have any mechanism to estimate the predicted pose confidence? For example, some docking models can also provide affinity prediction or likelihood of the predicted poses.\n\nLIMITATIONS:\nThe limitations have been described.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}