DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
Authors: Jiaxian Yan
ZAIXI ZHANG
Jintao Zhu
Kai Zhang
Jianfeng Pei
Qi Liu
Venue: NeurIPS 2024 poster (2024)
Keywords: Molecular Docking; Binding Pocket Prediction; Contrastive Learning; Iterative Refinement
Primary area: machine_learning_for_other_sciences_and_fields

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
Jiaxian Yan', Zaixi Zhang', Jintao Zhu?, Kai Zhang', Jianfeng Pei?, Qi Liu
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China CCntee for Quantitative Biology,
Academy for Advanced Interdisciplinary Studies, Peking University
{jiaxianyan, zaixi, sa517494) Amail. ustc. edu. cn, zhujter zhujt@stu. pku. edu, cn, jfpeidpku. edu. en, mallugglustt...mmm edu.cr
Abstract
Molecular docking, 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 the first stage. Then we follow bi-level coarse-to torsse--to-fiee 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-arr GDL. model DiffDock. With the consideration of physical validity, this improvement increases to about 300%
Introduction
Recent advancement in geometric deep learning (GDL) 1 2 3 presents an innovative and promising
molecular docking paradigm 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 either regression or generation task, eliminating the need for intensive candidate sampling [8. T0). 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- mean-square deviation (RMSD) metric, and the inference efficiency [2].
According whether prior pocket given, molecular docking can be divided into blind and site-specific docking
[13]. Traditional sampling methods adeptly navigate both scenarios, primarily differing the scope of the search space they explore. 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 [4] and DiftBinndI-R [15] concentrate on site-specific docking and only protein atomic level structure within defined radius (usually 6-12 Å) of the
"i Liu is the corresponding author.
1 All codes
will released on httpppps//gtttoo.oco///aaaaaaa/p
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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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 [[6] 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. [T6] proposed the PoseBusters test suit 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-performet 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, 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 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 coarse- to-fine iterative refinement module Bi-EGMN. This module prioritizes the residue-level structure covered by a large outer box 4Fig[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) training objective imposing penalties for steric clashes and constraints on ligand mobility, and (iii) a rapid pess-processing step composing torsional alignment and energy minimization for structure correction.
To accommodate two different docking settings, DeltaDDok 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 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 boeket-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 components, namely the physics-infome 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-ligame pair. With the consideration of physical validity, this improvement increases to approximately 300% on the PoseBusters benchmark.
2

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Ligand Malecule
Candidate Pockets
b)
Bleevel Iterative Refinement
Fast Structure Correction
Final Output
Adjusing inglt
pose
pasa
Ligand
Ercyding
Encoder
Tanturn gpird
Bustrgliia giod
m+
Minimicatiin
Leorning
Ugann clee Pusten-ccatton Patenazom
Boandarr CodRE Box
Maxitumr distance lolggand stoms
The overview of DeltaDock's two modules. (a) The pocket-ligand alignment module CPLA. Contrastive learning adopted to maximize the correspondence between target pocket and ligand embeddings for training. During inference, the pocket with the highest similarity of the ligand selected. (b) The bi-level iterative refinement module Bi-EGMN. Initialized with a high-quality sampled pose, the module first performs a coarse-to-Pi fine iterative refinement. This process generates progressively refined ligand poses utilizing a recycling strategy. To guarantee the physical plausibility of the predicted poses, two-step fast structure correction is subsequently applied. This correction involves torsion angle alignment followed by energy minimization based on the SMINA.
Figure 1:
2
Related Work
2.1 Sampling-basee Docking
Traditional docking methods, epitomized by the likes of VINA [88 and SMINA [9), operate on 'sampling-and-4- 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 speed increase of an order of magnitude compared to prior CPU-based methods.
2.2 Geometric Deep Learning-basee based Docking
GDL introduces a new paradigm in molecular docking, where the sampling process is bypassed by interpreting molecular docking either regression task or 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 methods, especially recent
valid structures [6]. Compared with physically reliable sampling-te 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 com- putational 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 freand-free approaches and focus 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

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3 DeltaDock Framework
3.1 Preliminaries
Notations. In this work, the separate structures of a protein P and ligand L are used as inputs (Fig. D. Both molecules are initially encoded as graphs, and we denote a molecule graph as (VEE), where V and & represent the node set and edge set respectively. Each node E V is associated with a coordinate 2; and a feature vector h4. Each edge (i,5) E E associated with an edge feature vector eij. For the ligand L and ligand graph G of represents the i-th atom in the ligand and aff corresponds to the atom's '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 GP, and the other is the protein residue graph GP GP contains protein atomic-level information similar to ligand graph G while G' contains protein residue-level information and overlooks the side-chain structure information. In GP^, of represents the i-th residue in the protein and x²"" corresponds to the C. coordinate of this residue. Details of the graph construction can be found in Appendix AA6
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. D DeltaDock comprises modules: a pocket- -igand alignment module CPLA responsible for selecting binding pocket from a pocket candidate set, and bi-level iterative refinement module Bi-EGMN dedicated to executing site- tite-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 generate candidate pocket sets, and then map these pockets and the target ligand into the same embedding space. The correct pocket embedding 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 generate 3D conformer of the input ligand, as depicted in Fig. 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 = {S1, .2... where s; represents the geometric center of i-th binding site. For CPLA, the protein pocket p; is defined as the residues within 15.0 À to
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:
=F= = -VVP=")))"'
(I)
where H is the ligand embedding matrix of shape IV and HP is the protein residue embedding matrix of shape |VPp| x d. The ligand representations m² and pocket representations m. are then obtained by pooling ligand nodes embedding and pocket nodes embedding:
m S vC), m Sum(H .V.").
(2)
where V is the protein node set of i-th pocket p. and the pooling operation sum pooling. For the pocket encoder, we input the entire protein residue graph GP rather than just the protein pocket residue graph, to incorporate global protein information into the pocket representation.

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3.2.3 Contrastive Embdding Alignment
With ligand representation mf and pocket representation m in hand, we calculate the cosine similarity score:
m m
5 |m
ml
For the candidate pockets S = {Si, $2.... the similarity score 4 between the target pocket and the ligand is expected to be higher than others. Thus, propose the contrastive learning objective:
exp(s+/T) exp((s/TT)
L N log
where 7 is the temperature paramter. For blind docking, the pocket with the highest similarity score with the ligand selected for the next docking step.
3.3 Bi-level Iterative Refinement
With a binding site < 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.
3.3.1 Inital Structure Sampling
For an iterative refinement module, an initial structure needed as a starting point. Previous work molecular 3D conformer generation [17] demonstrates the importance of good initial structure. Therefore, Bi-EGMN adopts a rapid GPU-accelerated sampling method proposed by Huang et al. [33] sample a high-quality initial X In this work, the search steps number and the search copy numher 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,, we iteratively update it to improve its accuracy. As discussed in [ the modeling of an entire binding pocket structure 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 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 Fig. [
Bi-level strategy. The first refinement level 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 Previous work demonstrates such range is large enough cover the binding pocket [30 In this context, as the full-atom structure of proteins is not considered, the pocket residue graph G is adapted. The second level is the atomic level, where we 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 for modeling the fine-grained interaction. The ligand coordinates X output by the last layer of atomie level refinement correspond the final predicted structure E
Bi-EGMN Layer. The bi-level E(3) equiivrair graph matching network (Bi-EGMN) layer is the model designed to calculate the protein-ligane 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 meraage function works extract messages Mi,j and m,, between node and its neighbor nodes from the same molecule graph. ma is later used for the updating of node features and m,y for the updating node coordinates. v(i,j) E Ep U U.C,, this function can formally written as
110 = =110;
10, mij = (m (1) ( (.j) majj = =xx;0) - -10)) (m()))))) (5)
5

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where d is the relative distance between node i and node j. and is a MLP.
The inter-message function works to extract messages Má.jj and fua between a node i and its neighbor nodes/ from the other molecule graphs. Formally, Vi Vp Ve or Ve.j Vp:
Pij = (u( h d Pi.j - (a: x ) (p(()))
(6)
After extracting inter-message and intra-messaee, the aggregation function aggregates the neighbor messages of the node Vi Vo UV::
m4 Σ JEN(()
m4 m,
Σ JEN(S)
maj.
d
+1
M4
Σ
((ua)))
pij. P
Σ
Pa..
(8)
0i. +1
JE (1)
)EN! (1)
where (1) is the neighbor of node in the same graph, and A, (t) is the set of nodes associated with node in the other graph.
Finally, the update function updates the position and features of each node:
zi [1+1] (U)
112;; + (1 -- (1)
mi joa, Vi Vc.
h (111) = (1- ()- -n + 3- (ph) m,, Mi, ki, ()),, Vi V
(10)
where 33 and 7) are feature skip connection weight and coordinates skip connection weight, respec- tively. Through such message- passsgg 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 DifiDock ( and other campling-basede methods, it is crucial ensure the plausibility of bond lengths and bond angles of the updated structure X²= Therefore, fast structure correction steps, torsion alignment, and EMINA-based energy minimization are designed.
Torsion Alignment. We employ a rapid torsion alignment for the updated structure. The target of this alignment is align the input structure x with the updated structures & by rotating its torsional bonds. Formally, let (b,, ej) denote a i-th rotatable bond, where 6; and c; are the starting and ending atoms of the bond, respectively. We randomly select a neighboring atom a, of and neighbor atom d of c; to calculate the dihedral angle 8; L(a,brc,, bic,d.) based on updated structure coordinates X Subsequently, we rotate the rotatable bond (b,, c4) of input structures to match its dihedral angle 5, the same as d,- 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 & This process ensures the plausibility of bond lengths and bond angles in the torsionally aligned structure PCT.
Energy Minimization. To further enhance the reliability of DeltaDock, we implement an energy minimization on the torsionally aligned structure & when an inter-molecular steric clash between the protein and ligand detected. This energy minimization is conducted using SMINA [19), as it is 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 &C"
3.4
Training and Inference
3.4.1
CPLA
The training object L is contrastive object defined before (Eq. 4). For protein and its candidate pockets set S = S2, ...). 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 (DCCmin) between all candidate pockets and the ligand. DCCmin & 5.0 Á, we add the ligand center into to assert the existence of positive pairs for every protein (details see Appendix B.3.I

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Table 1: Blind docking performance on the PDBbind dataset. All methods take RDKit RDKit-generiie generated ligand structures and holo protein structures as input, trying to predict bound complex structures. DeltaDock-SC refers to the model variant that generates structures without implementing fast structure correction. DeltaDock-Randee
refers the model variant that generates structures without high- quality initial poses. The best results are bold, and the second best results are underlined.
Time Split (363)
Timesplit Unseen (142)
Time average
Method
RMSD % below
Centroid
below
RMSD
% below
Centroid % below
Seconds
2.0Á
5.0A
2.0A
5.0À
2.0A
5.0A
2.0A
5.0A
QVINA-W
49"
20.9
40.2
41.0
54.6
15.3
31.9
35.4
47.9
GNINA
393
21.2
37.1
36.0
52.0
13.9
27.8
25.7
39.5
VINA
119
10.3
36.2
55.2
7.8
25.5
41.8
SMINA
146*
13.5
33.9
38.0
55.9
9.0
25.7
29.9
41.7
GLIDE
1405*
21.8
33.6
48.7
19.6
28.7
40.6
DSDP
1.22
40.2
59.0
59.5
78.2
37.3
54.9
55.6
71.8
EquiBind
0.03
5,5
39.1
40.0
67.5
0.7
18.8
16.7
43.8
TANKBind
0.87
17.6
57.8
$5.0
77.8
3.5
43.7
40.9
70.8
DifiDock
80
36.0
61.7
62.9
80.2
17.2
42.3
43.3
62.6
FABind
0.12
33.1
64.2
60.8
80.2
19.4
60.4
57.6
75.7
FABind+
6.4
43.8
73.3
59.1
86.2
34.7
63.2
57.6
75.7
DeltaDock-SC
2.58
47.9
68.0
83.2
40.8
60.6
65.5
78.9
DeltaDock
2.97
47.4
66.9
83.2
40.8
61.3
78.9
The time of consumption is denoted with * if only consumes CPU.
All results of baselines ure taken from [1] for fair comparison.
3.4.2 Bi-EGMN
We design physics-nfformed loss function for the Bi-EGMN module for training. The coordinates X and x output by the last layer of atomic level and residue level are both employed in the computation of this loss. Formally, the loss function be expressed as follows:
Linter + Ai + A2 + AzLiboudd
(11)
where À are weight hyper-parameters. Among the four components, inter-distance map loss Tinter is responsible for the RMSD) accuracy. Other three items, namely intra-distance map loss Lintra- vdw constraint loss Tedve and bound matrix constraint loss Lhound 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).
Experiments
4.1
Settings
datasets in this work. Our model is trained on the PDBbind dataset, where the training, validation, and testing set are constructed based the time split strategy used in previous work [II]. PoseBusters, which contains 428 carefully selected data released from January 2021 to 30 May 2023, directly adopted to evaluate the ability predict physically valid poses.
Dataset. We conduct experimetns PDBbind [34 v2020 and PoseBusters [16
Evaluation. Root-mean- Root-mean-squure--eee square-deviation (RMSD) and centroid distance (CD) are used to evaluate the docking accuracy of different docking methods, and the PoseBusters [66] test suite is employed to evaluate the performance predicting physically valid poses. Additionally, as pocket prediction plays important role in framework, the distance between the center of the predicted pocket and the center of the ground-trum -trth 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.
7

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a)
Percentage of predictions
60%
31.5%
4..%%
40
40%
36.%
20.48
20
20
14%
Equatirdd
rateratrnd
Dantleck
DROP
Datattock
DeltsDork
Eqpattord
Feblidk
Dellatock de
DeltaDack
Figure 2: Site-specific docking performance. (a) Overall Performance of different methods on the PDBbind test set. The search space was delineated by extending the minimum and maximum of the y; and coordinates of the ligand by 4 Å respectively. For TANKBind, directly supply the protein block with radius of 20 Á centered around the ground-truth ligand center to the model. (b) Overall performance of different methods on the PoseBusters dataset. (c) waterfall plot for illustrating the PoseBusters tests filters for both DeltaDock and DeltaDock-SC predictions. The evaluation results for Dock are denoted above the lines, while those for DeltaDock-SC are annotated below.
4.2.1 Blind Docking
As demonstrated in [ DeltaDock outperforms all baseline methods. Specifically, Dock achieves a remarkable success rate of .44 (where RMSD < 2.0 Å), surpassing the previous SOTA GDL method, DifiDock, which has 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-banl superior performance across all metrics. Notably, as elucidated in Section 3.3 DeltaDock employs the sampling algorithm DSDP for generating the initial structure. Yet, framework allows DeltaDock to significantly outperform DSDP.
method in the PDBbind test set, DeltaDock still exhibits
Beyond accuracy, efficiency is critical performance measure for molecular docking methods. As indicated in Table [ DeltaDock maintains competitive level of efficiency, despite the inclusion of an energy minimization operation enhance accuracy and reliability. Molecular docking methods invariably face a trade-off between efficiency and accuracy. However, the data presented in Table suggest that DeltaDock could serve as 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, 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 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% 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 outperform GDL models in site-specific docking tasks (35). However, as evidenced by the results presented in Table I performance both blind and site-specific docking scenarios, demonstrating its versatility and robustness in handling diverse docking settings.
and Fig DeltaDock exhibits superior
4.3 Evaluation of Pose Validity
We further investigate DeltaDock's '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 checks, encompassing chemical validity and consistency, intramolecular validity, and intermolecular validity. When physical validity

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RMSD upcated posess
18
CRA Tip Foces
CPAA Tip- Fookt
PDBaind Test Seet Success Rats
Fitekh by Noike
Success Ratle
FiBwch No
Saalig Pocter
Eivele Neceet
DCC
Cube side longth
RMSD of init
5
ONATTo Poom
(if (ira- Parset
Success Ralee
RMSD updotood
PoseBuster
29
Facder lus
Fyccaa tacket bniag Pocket
Batkiriig hader
DCC
Cube side length
RMSD of initia pases
Figure 3: Further analysis on the (a) PDBbind and (b) PoseBusters dataset. Left: DCC cumulative curve of top-1 pockets. Middle: VCR cumulative curve of top-1 pockets. Right: Scatter plot of RMSD of initial and updated poses, All experiments are conducted in the blind docking setting.
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, without the application of the fast structure correction step, shows significant improvement over previous methods. These results substantiate DeltaDock's 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- 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 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
Table Results ablation study.
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 de- tails). illustrated Table becomes clear that each component, encompassing CPLA and the bi-level strategy in Bi-EGMN, plays a signif- icant role in enhancing the overall performance of Deck.Dock. Due to the space limitation, a full ablation study can be found in Appendix. C.3
RMSD % below 2 Å PDBbind PoseBusters
Method
DeltaDock
47.4
49.3
w/o CPLA w/o Bi-EGMN
41.2
43.7
44.6
41.8
w/o Residue Level
44.6
44.4
w/o Atom Level
44.6
42.1
5 Conclusion
In this work, we proposed DeltaDock, unified framework for accurate, efficient, and physically reliable molecular docking. DeltaDock was two-stage docking framework. consisting of pocket prediction and site-speen site-specific docking. We innovatively reframed the pocket prediction task as a pocket- ligand alignment problem and then followed hybrid strategy to jointly utilize both GDL and physics- informed traditional algorithms for site-specific docking. Comprehensive experiments demonstrated the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieved 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.
Acknowledgements
We extend gratitude to the reviewers for their valuable and insightful feedback, which significantly improved this work. We are also grateful to Lixue Cheng from Microsoft Research for her helpful suggestions and comments. This research was supported by grants from the National Natural Science Foundation of China (No.62406303, No.623B2095), the National Key R&D Program of China (No.2023YFF1205 the Anhui Provincial Natural Science Foundation (No (No.2308085QF229, and the Fundamental Research Funds for the Central Universities.
10

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A More Detailed Descriptions
A.1 Dataset Preprocessing
We follow the time split strategy used in previous work [8] 30 [1 to split the dataset to construct the train, validation, and 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 docking methods is evaluated on the time spit test set following previous works. In this work, we only select the protein chains within 10 to the ligand structure.
All compounds discovered or after 2019 are the and validation
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 we statistically analyze the PDBbind time-split test set and count atom numbers in proteins. Notably, the number of atoms escalates substantially as the cutoff value increases.
be observed that
Table 3: Statistics of the PDBbind time split test set.
Average Number
Maximum Number of
Data
Number of Number
Entire protein structure
322
2.536
1,488
11,697
Structure within 40.0 Å cubic hox centered on the ligand
179
1,602
400
3.055
Structure within 15.0 Á from ligand
111
1,050
213
1,944
Structure within 12.0 from ligand
73
740
164
1,582
Structure within 8.0 Å from ligand
379
75
986
Structure within 6.0 A from ligand
207
45
$48
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.
Figure 4: The main protease of SARS-CoV-2 is depicted by the white surface. The ligand structures in pink, blue, and red correspond to PDB 5RGY, 7AQJ, and 7JU7, respectively. Left: The green pocket, a protein structure truncated to within 12.0 Á of the blue structure, is insufficient to encompass the pocket structure necessary for predicting the red structure. Right: The orange pocket, truncated within a 40.0 À box utilized by DeltaDock, is ample to cover the entire pocket.
A.4 Analysis of Existing Pocket Prediction Methods
As depicted in Fig existing pocket prediction methods generally achieve hit rate of approximately 70%-80% where the distance between the predicted pocket center and ligand center (DCC) 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 ready-to-dock ligand and a candidate pocket set derived from a suite of existing pocket prediction models.
We further statisties how many pockets these methods predict in Fig. We observe that Fpocket and DoGSite3 39) output much more pockets than DSDP 23 P2Rank [25). and SiteMap 40)

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Schrödinger
P2RANK
Sucoess
Epocket
DoGSiteScorer
DSDP
Candidate Pockets
00C
Figure 5: Performance of different pocket prediction methods on the PDBbind test set. The hit rate significantly improved by ensembling the predicted pockets from various methods.
Combining information from Fig. and Fig 5 it evident that the pockets predicted by DSDP and P2rank are highly druggable. Other methods, in contrast, tend to predict many non-druggable pockets.
50
Num of pockets
40
30
20
10
-10
DSDP
P2Rank
Fpocket Methods
SiteMap
DoGSite
Figure 6: Pocket numbers violin plot of different methods. Pocket prediction methods generally predict series of druggable pockets.
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 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 tens of minutes per protein-ligant pair, as illustrated in the Table. below.
For molecular docking, efficiency is crucial, and specialized methods such as OpenMM can be excessively time-consuming. What's more, is important to note that SMINA, although generally regarded as docking method, not employed for docking in our workflow but rather utilized in its minimization mode for energy minimization.
Table 4: Efficiency Comparison between SMINA and OpenMM.
Methods
Time (per pootein-ligand pair)
SMINA OpenMM
about 0.4 seconds
several minutes to of minutes
15

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A.6 Graph Construction
Ligand Graph. The input ligand L is first represented as a ligand graph g = (Ve. EL). where y 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 of is also associated with an atom coordinate æ.f retrieved from the individual ligand structure P and an atom feature vector hf. The edge set g is constructed according to the spatial distances among atoms. More formally, the edge set is defined to be:
= : | zf1² < cut Vi.j V VV)).
(12)
where e is a distance threshold, and each edge (i,j) E Ed associated with an edge feature vector e.j. 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" is constructed in the same way the ligand graph.
Protein Residue Graph. For protein residue Graph GD -VVP- PPP-.EP*). vP+ is the node set and the node represents the i-th residue in the protein. Each node vf" is also associated with C. coordinate of the i-th residue z!"* retrieved from the individual protein structure and residue feature vector h,p The edge set P* is constructed according to the spatial distances among atoms. More formally, the edge set is defined be:
_- ((i.)):la² =0, <cul Vi,J E VP
(13)
where cut P* is distance threshold, and each edge (i.j) E Ex associated with an edge feature vector e/j 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 33). DiffIDock [9], DeepDock 44
Uni-Mol 14], FABind [TT, and FABind+ (45) and traditional sampling methods, VINA [8].
SMINA 19 and DSDP (23 are used as baselines. According to their performance
both
PDBbind and Posebusters, Dift 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 DoGiite3
33 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:
RMSD
(af
af')2,
(14)
i=1
Centroid
of
(15)
where r.f is the ground truth coordinate of i-th ligand atom and &f' 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 [6).
For pocket prediction, the DCC metric is defined as:
V of
DCC =
(16)
16

---

where é is the predicted pocket center. As for the VCR metric (23] we calculate the cube side length of 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 single GPU takes approximately hours, while the Bi-EGMN requires about 48 hours on 4 GPUs. To determine the hyperparameters, we performed 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 0.0003 and an L2 regularization factor of 10-6, The learning rate was scaled down by 0.6 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- and 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 candidate pockets set S (2,...,...) we establish maximum pocket number, Nmarx to construct negative pockets for data augmentation. If [[ >= Nmaz. we select the top-N pockets the sort of DSDP, P2Rank accordingly. If ||| < NMaas we randomly select (Nmar |S|) C. atoms that are more than 20.0 Á 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 given molecule is unknown in practical scenarios, 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.
aim to train pose-robust CPLA model. To achieve
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 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 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 L2 regularization factor of 10 employed for training Bi- 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:
Linter + Lintro + > Legn + AaLioound-
(17)
As introduced before, the inter-distance map loss Linter is responsible for the RMSD accuracy. Other three items, namely intra-distance map loss Lântras vdW constraint loss Lendws and bound matrix constraint loss Lbound employed for physical validity.
17

---

Table 5: The hyperparameter options searched through for CPLA. The final parameters marked
bold.
Parameter
Search Sapce
Number of layers
2, 3, 4
Batch Size
8, 16, 32, 64, 128
Dropout
0.1
Learning rate
0.003, 0.001, 0.0003, 0.0001
Max pocket number for training Pocket used for training
Null, 16, 32, 64, 128 [DSDP, P2Rank] Intra-protein, Cross-protein
Training loss
ESM2-3B embedding
True, False
AFP hidden dimension
64, 128, 256
GVP node scalar hidden dimension
32, 64, 128
GVP node vector hidden dimension
12, 16, 32
GVP edge scalar hidden dimension
32, 64, 128
GVP edge vector hidden dimension
12, 16, 32
The two distance map losses can be formally expressed as:
Linter
Σ
apreed
dgg 11 Lintra = Σ Σ
||
deg
(18)
VEVC JEVO
ieV: JEVe
where predicted distance derred ||a:pred between node and j are calculated based on node coordinates.
s.y 2 and ground-truth distance di
||x:?" xx3
The other two physics-informed losses can be formally expressed as:
Livdu
Σ
marldije poed 0),
(19)
iEVe JEVP
Lbound
Σ
Σ
(a (dwd -dl dered, ed ,0) max (dpred ded.up 0),
(20)
iEV
JEVE
where the vdW distance dzdw 0.75(r + +ryd is calculated based node van der Waals radii wedw As for the lower bound distance pod. and upper bound distance aid.up. they are determined based on the bound matrix generated by RDKit [49 following [66]
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 two-step process: (1) the minimum and maximum of the x, and coordinates of the ligand are extended by 4 Á each; (2) if the box size is less than 22.5 Á 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 A, deviating from the adaptive strategy employed during training. For each epoch during training, pose is randomly selected. This pose augmentation strategy significantly amplifies the diversity the input. As depicted in Fig the sampled poses can nearly encompass the entire pocket cavity.
Recycling. During both training and inferencing, the recycling strategy 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 fixed to 4.
B.4 Ablation Studies Settings
w/o CPLA: pockets predicted by DSDP employed to perform the following predictions.
w/o Bi-EGMN: the sampled structures are directly employed as final structures 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.
18

---

5d1n
(b) 518a
Figure 7: Initial pose augmentation. During the initial pose augmentation phase of training the Bi-EGMN, we randomly select one pose from all sampled poses for each epoch. This selection strategy ensures that the training initial poses can comprehensively cover the entire pocket.
Table 6: The hyperparameter options we searched through for Bi-EGMN. The final parameters are marked in bold.
Parameter
Search Sapce
Recycle
True, False 32, 64, 96, 128 4, 6, 8, 10 8, 16, 32, 64 0.1
Hidden dimension
Number of layers for each level Batch Size
Dropout
Learning rate
0.001
Initial pose augmentation
True, False Adaptive, 30.0 Á
Pose sampling box size
CPLA pockets used for sampling Protein structure level
Top-1, Top-2, Top-3, All
Atom level, Residue level, Bi-level True, False
ESM2-3B embedding for residue level
C More Experimental Results
C.1 Binding Pocket Prediction
C.1.1 Overall Performance on PDBhind
In addition to the overall performance presented in Fig 3 we offer a more detailed analysis in Fig 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 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, 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.
Table 7: Performance of employing different candidate pockets train CPLA
Pockets
% of DCC A
DSDP
64.46
P2Rank
55.37
DSDP + P2Rank
69.97
DSDP + P2Rank + Fpocket
65.84
19

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FDEbind Tes: Set.
PDBbind Useen Test Set
POBbind Test Set
PDBbind Useen Test Set
bap food
5 Taie
(M00 le. nerer
Coclte hip meceet
DCC
DC2
Case side benght
Cube langth
POBbind Test
idS Useon Tast Set
POBsind Text Set
POBbind Usewn
Kile uul hosmn
SuccEn R210
arr ruter
Succes
Succem
DCC
3cc
Cuse ride lenth
Cabe ode length
POBlind Test: Set.
id Useon Tast Set
POBbind Test Sat
POBbind Useen Test Set
CRa
Succon Rr1
(c)
Ducree
000
30C
Cuze alde lingt
Cabe langth
Figure 8: Performance of binding pocket prediction models on PDBbind dataset. (a) Comparison between top-1 pockets predicted by CPLA and top-1 pockets predicted by other methods. (b) Comparison between top-1 pockets predicted by CPLA and best pockets among all pockets predicted by other methods. (c) Comparison between top-2 pockets predicted by CPLA and best pockets among all pockets predicted by other methods.
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 co-crystal ligand structure and an -eKir-generated ligand structure, showcasing the model's 's resilience to ligand poses and the effectiveness of our strategy.
Table 8: Influence of ligand conformations on CPLA
Input ligand pose
% of DCC < Á
Co-crystal RDKit-generated
70.25
69.97
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 ggandddependent, such as FABind. To further demonstrate the efffectivneess of CPLA, a comparison on pocket prediction accuracy is conducted between FABind and CPLA as shown in Table. g Obviously, our model achieves significant advantage.
Table 9: Comparison with FABind
Methods
% of DCC < 3.0 Á
% of DCC 4.0 Á
FABind
42.7
56.5
CPLA Top-1
54.8
70.0
20

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C.2 Blind Docking Performance on PoseBusters
Due 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.
DeltaDock DeltaDock-SC
0 -212
100%
0
80%
60%
0
0
0
0
-2
0
0
3
2
40%
0
-1
25
0
0
150
0%
beet AND
at aopetieen:
Dhame conse booghe nc aroon
dash netional
ditave vendey rotto
regent fhemn tatt
Set serr
Nercou
Figure Blind Docking Performance on PoseBusters.
C.3 Detailed Ablation Studies
Comprehensive ablation experiments were performed within 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: (I) 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 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.
Table 10: Blind docking performance on the PDBbind dataset.
Time Split
Timesplit Unseen (142)
Method
RMSD & helow Centroid & below
RMSD
below
Centroid below
2.0A
5.0A
2.0
5.0A
2.0A
5.0A
2.0
5.0A
DelraDock
47.4
66.9
66.7
83.2
40.8
61.3
60.4
78.9
w/o recycle
46.0
64.2
67.2
80.2
40.8
62.0
78.2
w/o Lune
46.8
65.3
66.4
81.3
40.8
62.7
64.8
78.2
w/o Lustiv
43.5
64.7
65.0
84.8
40.1
58.5
61.3
81.7
42.4
66.4
66.9
82.1
35.9
63.4
79.6
w/o lorsion aligament
47.9
68.0
69.1
82.9
41.5
62.0
62.7
78.9
w/o energy minimization
46.8
67.8
70.0
83.2
40.1
60.6
65.5
78.8
sampling"
16.0
53.4
w/o structure sampling, and structure conrection
19.8
55.6
56.2
82.4
21.1
52.8
52.1
77.5
w/o Lrouna Lintrss Luste structure conrection, structure sampling
30.0
63.8
65.3
82.9
28.2
53.5
57.7
78.2
No structure sampling means we directly pot RDKil-gencrated ligand structure at ocater of the protein as the initial stracture.
21

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C.3.2 Ablation Studies On PoseBusters
Fig. I0 and Fig. present ablation studies on PoseBusters explore the effect of physics-informed training items and structure correction step. From the figures, we can see that: (1) the physics- informed training items and structure correction step contribute the good physical validity of Dock (2) Among the physics-informe training items, Lintra is especially important for the GDL model to predict valid structures without post-processing. These results demonstrate the effectiveness of DeltaDock.
DeltaDock DetaDeck wi torsion align
DeltaDock
103%
D-1RD
DetaDeck wi energy min mintianion
02%
92%
60%
0-00-11
67%
60%
20%
20%
Figure 10: Site-specific docking performance on the PoseBusters dataset.
DeltaDuck-SC
DeltaDock
DetbDook SC
DehaDock
02%
40%
00%
25%
20%
36
0%
DeltaDockSCC
DeltaDock
DetaDock SC
DetaCook wi
60%
D 24
183
40%
42%
20%
20%
0%
0%
DeltaDock-SC
DeltaDock
100%
-180
DetaBook SC w/o Lawer
100%
DetaDock WD
40%
38
60%
306
0
40%
40%
20%
20%
0%
Figure 11: Site-specific docking performance the PoseBusters dataset.
22

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Table 11: Blind docking performance the PDBbind dataset. The best results are bold, and the second best results are underlined.
Ligand
Centroid Distance
Method
Time average
Percentiles
% below Threshold
Percentiles
St helow Threshold
Seconds
25%
50%
75%
mean
2A
5A
25%
50%
75%
mean
2
QVINA-W
2.5
13.6
GNINA
393
2.8
8.7
21.2
13.3
21.2
37.1
1.0
4.5
21.2
11.5
36.0
$2.0
VINA
119
3.7
6.8
14.6
10.1
10.3
36.2
4.1
13.3
8.2
32.1
55.2
SMINA
146
3.8
8.1
13.5
12.1
13.5
33.9
1.3
3.7
16.2
9.8
38.0
55.9
GLIDE
1405
2.6
9.3
21.8
16.2
21.8
33.6
5.6
26.9
14.4
36.1
48.7
DSDP
1,22*
1.0
3.0
7.9
7.2
42.4
59.8
1.0
4.8
4.9
60.3
75.8
EquiBind
0.03
3.8
6.2
10.3
8.2
5.5
39.1
2.6
7.4
67.5
TANKBind
0.87
2.4
4.2
7.5
7.8
17.6
57.8
0.8
1.7
4.3
5.9
55.0
77.8
DiffDock
80"
3.5
74
7.4
36.0
3.3
80.2
FABind
0.12*
1.7
3.1
6.7
6.4
33.1
64.2
0.7
1.3
3.6
4.7
60.3
80.2
FABind+
0.16*
1.2
2.6
5.8
5.2
43.5
71.1
0.4
1.0
2.9
3.5
67.5
84.0
FABind+(40)
6,4%
1.2
2.4
5.3
5.1
43.8
73.3
1.0
2.6
3.5
69.1
86.2
DeltaDock-SC
2.58
2.3
5.7
47.9
2.6
DeltaDock
2.97
1.0
2.2
6.4
5.8
47.4
66.9
0.3
0.9
2.9
4.2
66.7
83.2
consumption
corresponds to time; absence this symbol, pertains time.
Table 12: Blind docking performance for PDBbind unseen dataset. The best results are bold, and the second best results are underlined.
Ligand RMSD
Centroid Distance
Method
Percentiles
below Threshold
Percentiles
& below Threshold
25%
50%
75%
mean
5
50%
75%
mean
2
QVINA-W
3.4
10.3
28.1
16.9
15.3
31.9
1.3
6.5
26.8
11.9
35.4
47
GNINA
4.5
13.4
27.8
13.9
27.8
10.1
27.0
15.1
25.7
VINA
5.0
12.8
25.5
6.1
17.8
10.9
24.1
41.8
SMINA
4.8
10.9
26.0
15.7
9.0
25.7
1.6
6,5
25.7
13.6
29.9
41.7
GLIDE
3.4
18.0
31.4
19.6
19.6
28.7
1.1
17.6
29.1
18.1
29.4
40.6
DSDP
1.0
4.5
10.0
9.1
37.2
54.9
1.5
6.5
7.5
54.2
69.0
EQUIBIND
5.9
9.1
14,3
11.3
0.7
18.8
2.6
6.3
12.9
8.9
16.7
43.8
TANKBind
3.0
8.7
52.1
2.3
4.4
7.2
41.5
DiftDock
2.9
12.0
17.2
42.3
2.7
14.2
9.8
43.3
62.6
FABind
2.2
3.4
8.3
7.7
19.4
60.4
1.5
4.7
5.9
57.6
75.6
FABind+
1.6
3.3
8.9
7.0
34.7
63.2
0.5
1.5
4.2
5.1
58.3
77.1
DeltaDock-SCC
1.2
3.9
7.8
5.7
40.8
60.5
0.5
1.2
4.0
4.1
65.5
78.9
DeltaDock
1.0
3.8
7.7
5.8
40.8
61.3
0.4
1.2
4.1
4.2
60.6
78.9
The time of consumption is denoted with if corresponds GPU time; in the absence of this symbol, the time pertains
CPU time.
C.3.3 Detailed Tables of Performance on PDBbind
In the main manuscript, we have opted present concise table Table. with key metrics due the space constraint, Here, we present the detailed tables Table. 11 and Table. 12 which encompasses all metries on PDBbind.
D Broader Impacts and Limitations
D.1 Broader Impacts
The development and maintenance computational infrastructure for Al-assisted molecular docking represent significant allocation of resources. Inefficient allocation underutilization of these resources can potentially result resource wastage.
D.2 Limitations
One disappointing limitation is the reliance external tools, such 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
23

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future, we will try to overcome this limitation by exploring pre- vre-training strategies on large-scale datasets generated by docking methods or recently developed AlphaFold3 [50].
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Reviews:
[{"review_id": "XhLzABsi24", "reviewer": "Reviewer_AHbE", "summary": "A 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.", "questions": "1. 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.", "limitations": "The limitations have been described.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "Originality:\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.", "weaknesses": "Originality:\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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer, thank you for your insightful comments on our papers and methods. Below are our responses to your questions.\n\n---\n\n> **For Originality 1**\n\nThank you for your insightful observation and suggestion. We acknowledge that Equations 7 and 8 in the manuscript may have been misleading regarding the weighting by interatomic distances. Taking $\\hat{m}\\_{i,j}$ as an example, this term can be further written as $\\hat{m}\\_{i,j}=\\frac{(x\\_i^{(l)} - x\\_j^{(l)})}{d\\_{i,j}^{(l)} + 1} \\phi\\_{\\hat{m}}(m_{i,j})$. $\\frac{(x\\_i^{(l)} - x\\_j^{(l)})}{d\\_{i,j}^{(l)} + 1}$ is a unit vector, whose direction is defined by $(x\\_i^{(l)} - x\\_j^{(l)})$. Therefore,The actual weighting of $\\hat{m}\\_{i,j}$ is determined by $\\phi_{\\hat{m}}(m\\_{i,j})$. We will revise the manuscript to explicitly clarify this point.\n\nAs for the skip connection (SK), it is an important component for DeltaDock's performance. Here, we present the ablation study of SK in the following table. It is obvious that the model without SK performs poorly.\n\n|Methods|Time Split||||Timesplit Unseen||||\n|-|-|-|-|-|-|-|-|-|\n||% RMSD < 2 Å|% RMSD < 5 Å|% Centroid < 2 Å|% Centroid < 5 Å|% RMSD < 2 Å|% RMSD < 5 Å|% Centroid < 2 Å|% Centroid < 5 Å|\n|DeltaDock|**47.4**|**66.9**|**66.7**|**83.2**|**40.8**|**61.3**|**60.6**|**78.9**|\n|DeltaDock w/o SK|41.9|63.4|62.5|80.7|38.7|57.0|56.3|75.4|\n\n> **For Originality 2**\n\nThanks for your valuable suggestion. FAbind+ is an updated version of FABind, with improvements in larger model size, pocket radius prediction, and so on.\nDespite this improvement, FAbind+ still considers residue structure only. In our work, we emphasize the \"difficulties in handling large binding pockets\" for methods that take atomic structure into consideration. \nHere, we analyze the pocket radius predicted by FAbind+ and we find that the radius predicted is generally smaller than 20.0 Å (about 93% data). According to the paper, the radius less than 20 Å will be adjusted to 20.0 Å, which indicates that the radius prediction of FAbind+ only works for about 7% of data points. \n\n|Method|Predicted Pocket Radius||||\n|-|-|-|-|-|\n||Mean|25% Percentiles|50% Percentiles|75% Percentiles|\n|Fabind+|13.0|10.9|12.4|14.6|\n\nThen, we perform experiments on the PDBbind and PoseBuster datasets. \nAccording to the result on the PDBbind dataset, although DeltaDock outperforms FAbind+ on % RMSD < 2 Å, we observe the promising result of FAbind+, especially for % RMSD < 5 Å.\n\n|Methods|Time Split||||Timesplit Unseen||||\n|-|-|-|-|-|-|-|-|-|\n||% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|\n|FAbind+|43.5|**71.1**|**67.5**|**84.0**|34.7|**63.2**|58.3|77.1|\n|DeltaDock|**47.4**|66.9|66.7|83.2|**40.8**|61.3|**60.6**|**78.9**|\n\nHowever, when it comes to the PoseBusters dataset, the performance of FABind+ drops significantly, which may be caused by the ignoring of atomic structure and physical constraints.\n\n|Methods|RMSD < 2.0 Å|RMSD< 2.0 Å & PB-Valid|\n|-|-|-|\n|FAbind+|25.0|0.0|\n|DeltaDock|**50.5**|**48.8**|\n\nIn summary, DeltaDock shows significantly more robust and promising results than FAbind+.\n\n> **For Quality 1**\n\nThanks for your valuable suggestion. We acknowledge the space constraints and have opted to present a concise table with key metrics in the main manuscript. Detailed tables, encompassing all metrics, are provided in the submitted PDF. We plan to incorporate the full table into the appendix of future versions as well. \n\n> **For Clarify 1**\n\nThanks for your valuable suggestion. We agree that removing the model blocks would enhance the clarity of our framework figure, and we are actively working on this improvement. \nAs for line 31, we want to express that these methods mainly focus on the blind docking setting, which can be used to find new drugable binding sites and explore unseen proteins. We will revise the sentence in the next iteration of our manuscript to ensure greater clarity and precision.\n\n> **For Question 1**\n\nThanks for your valuable suggestion. Previously, we directly selected the top-1 poses as the initial conformations. Here, we try to generate diverse poses by sampling different conformations. We sample top-n (n<=10) poses and select the best structures (minimum RMSD) predicted by DeltaDock-SC to calculate the docking success rate. Our findings indicate a significant improvement in the performance of the best-performing poses with an increase in the sampling number. This suggests that DeltaDock exhibits the capability to generate diverse poses. \n\n|Samples|PoseBusters||PDBbind||\n|-|-|-|-|-|\n||DeltaDock-SC|DeltaDock|DeltaDock-SC|DeltaDock|\n|Top-1|57|56|48|47|\n|Top-2|64|63|57|55|\n|Top-3|66|65|60|56|\n|Top-4|69|68|61|57|\n|Top-5|70|69|61|57|\n|Top-6|71|70|62|58|\n|Top-7|73|71|62|58|\n|Top-8|74|72|64|58|\n|Top-9|74|72|64|59|\n|Top-10|74|73|64|59|\n\n> **For Question 2**\n\nThanks for your insightful suggestion. Existing docking methods like DiffDock and FABind+ utilize pose confidence evaluation modules to identify optimal poses from a set of candidates. In our response to Question 1, DeltaDock exhibits the capability to generate diverse poses. At this time, we hypothesize that integrating a confidence model for pose selection after the generation of diverse poses by DeltaDock could further enhance docking accuracy.\nInspired by FAbind+ and DiffDock, we propose training a confidence model using a classification loss which aims to predict whether the RMSD between a pose and ground truth pose less than 2.0 Å. The training data for this model would be generated using the trained DeltaDock.\nWe are actively pursuing the implementation of this idea. While we anticipate promising results, please allow us a few more days to finalize our experiments. \n\n---\nOnce again, thank you for reviewing our paper and providing valuable suggestions, please let us know if you have any further concerns, and we are willing to answer any further questions you have.\n\nBest regards,\n\nPaper Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DanxF5xiXt", "reviewer": "Reviewer_KF4e", "summary": "This 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.", "questions": "Please refer to Weakness Section", "limitations": "Please refer to Weakness Section", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\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.", "weaknesses": "1.\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).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer，thank you for your insightful comments on our methods. Below are our responses to your questions.\n\n> **For Weakness 1:**\n\nThank you for your valuable comment. FABind is an effective method, particularly its inspiring framework that first predicts pockets and then performs docking. Recently, FABind+, an updated version of FABind, has also been proposed.  We compare DeltaDock with both FABind and FABind+ to provide a thorough analysis. \n\n**Key Differences and Advantages of DeltaDock:**\n\n1. **Pocket Prediction:** While FABind/FABind+ predict pocket residues, DeltaDock redefines pocket prediction as a **pocket-ligand alignment task**. This contrastive approach leverages established pocket prediction methods, leading to improved accuracy. As shown below, DeltaDock achieves higher accuracy in predicting ligand binding sites:\n\n   | Method | % of DCC < 3.0 Å | % of DCC < 4.0 Å |\n   |---|---|---|\n   | CPLA Top-1 | **54.8** | **70.0** |\n   | FABind/FABind+ | 42.7 | 56.5 |\n\n2. **Structural Detail:**  FABind/FABind+ prioritize speed by focusing on residue-level structure. In contrast, DeltaDock adopts a **bi-level strategy**, modeling both residue and **atomic-level protein structure**. This enables a more accurate representation of binding interactions.\n\n3. **Physical Constraints:** DeltaDock incorporates **physical constraints** in its training loss and utilizes a **fast pose correction** step. This ensures the physical validity of predicted poses, a feature absent in FABind/FABind+.\n\n**Performance Comparison:**\n\nThe aforementioned differences translate into superior performance for DeltaDock on both the PDBbind and PoseBusters datasets. \n\n**PDBbind:** While FABind+ demonstrates strong performance on PDBbind, particularly in achieving a high percentage of predictions with RMSD < 5 Å, DeltaDock consistently achieves higher accuracy in predicting poses with RMSD < 2 Å, a more stringent and relevant metric for accurate binding mode prediction.\n\n|Methods|Time Split||||Timesplit Unseen||||\n|-|-|-|-|-|-|-|-|-|\n||% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|\n|FAbind+|43.5|**71.1**|**67.5**|**84.0**|34.7|**63.2**|58.3|77.1|\n|DeltaDock|**47.4**|66.9|66.7|83.2|**40.8**|61.3|**60.6**|**78.9**|\n\n**PoseBusters:**  The performance of FABind+ significantly declines on the PoseBusters dataset, which is specifically designed to challenge docking methods with poses that appear reasonable but are physically implausible. Notably, FABind+ achieves a 0% success rate when considering physical validity (PB-Valid). This suggests potential overfitting to the PDBbind dataset. DeltaDock, on the other hand, maintains high accuracy, demonstrating its robustness and ability to generalize to challenging cases.\n\n| Method | RMSD < 2.0 Å | RMSD< 2.0 Å & PB-Valid |\n|---|---|---|\n| FAbind+ | 25.0 | 0.0 |\n| DeltaDock | **50.5** | **48.8** |\n\n**Conclusion:**\n\nWhile FABind and FABind+ are valuable contributions to the field, DeltaDock's innovative approach to pocket prediction, its consideration of detailed structural information, and the incorporation of physical constraints result in a more accurate and robust method for protein-ligand docking. \n\n> **For Weakness 2:**\n\nThank you for raising this important point regarding the choice between end-to-end and hybrid frameworks for molecular docking. While end-to-end deep learning models have gained significant traction, their direct application to molecular docking, particularly for site-specific scenarios, presents unique challenges. \n\nFor instance, blind docking methods like FABind and FABind+, while innovative, often struggle with site-specific docking. Their reliance on pre-predicted pocket information for feature embedding limits their ability to accurately model the complex interactions within a defined binding site. \n\nDeltaDock, in contrast, adopts a hybrid approach that leverages the strengths of both deep learning and established molecular docking tools. This strategic integration offers several advantages:\n\n* **Versatility:** DeltaDock seamlessly handles both blind and site-specific docking scenarios, overcoming the limitations observed in purely end-to-end methods.\n* **Accuracy and Generalizability:** By incorporating well-established tools and physics-based principles, DeltaDock achieves superior predictive accuracy and demonstrates robust generalization capabilities across diverse datasets.\n* **Balance between Speed and Accuracy:** While not as rapid as some end-to-end methods, DeltaDock achieves a docking time of 2-3 seconds per ligand, striking a balance between computational efficiency and predictive power. This speed, coupled with its enhanced accuracy, makes DeltaDock a practical solution for high-throughput virtual screening campaigns.\n\nIn conclusion, while the pursuit of a fully end-to-end docking framework remains a worthwhile endeavor for the field, hybrid approaches like DeltaDock offer a currently more effective solution for molecular docking. \n\n> **For Weakness 3:**\n\nThank you for your valuable suggestion. Addressing the complexities of flexible docking is undoubtedly crucial. DeltaDock's framework possesses the flexibility to accommodate such scenarios.  One viable approach is integrating sampling techniques specifically designed to account for flexibility, such as DSDP-flex[1].  Furthermore, enabling protein coordinate updates during the structure refinement phase allows for greater conformational exploration. Indeed, expanding DeltaDock to directly incorporate flexible docking is a high priority in our future research endeavors. \n\n[1]. DSDPFlex: An Improved Flexible-Receptor Docking Method with GPU Acceleration, ChemRxiv, 2023.\n\n---\nOnce again, thank you for reviewing our paper and providing valuable suggestions, please let us know if you have any further concerns, and we are willing to answer any further questions you have.\n\nBest regards,\n\nPaper Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hxievIHTz7", "reviewer": "Reviewer_qFb1", "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.", "questions": "what is the distance threshold used for graph construction?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The model is faster than the diffusion-based method DiffDock.\n2. 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.  \n2. 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.  \n3. in Line 60, \"a GPU-accelerated pose sampling algorithm generating high-quality initial structure\" is just DSDP, which limits the novelty of the paper.  \n4. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer, thank you for your insightful comments on our dataset, figures, and methods. Below are our responses to your questions.\n\n---\n\n> **For Weaknesses 1**\n\nThank you for raising this important question.  This work utilizes the established approach of training on the PDBbind time-split training set and testing on both the PDBbind time-split testing set and the PoseBusters dataset. \n\nWhile the time-split strategy of PDBbind, first employed by EquiBind, aims to reflect real-world application scenarios with varying data quality, a detailed sequence identity analysis for this approach was not provided by the authors. \n\nTo address this, we employed mmseqs2 to analyze the sequence identity between the PDBbind training and testing sets. Our findings, presented in the table below, indicate an average sequence identity exceeding 0.4 for both test sets. Notably, the \"unseen test set,\" where protein UniProt IDs are absent from the training set, exhibits a lower average sequence identity compared to the complete testing set. This observation sheds light on the significant performance drop observed for all previous GDL docking methods on the unseen test set, highlighting the increased difficulty posed by novel proteins.\n\n|PDBbind Set|Average Sequence Identity|\n|-|-|\n|Train-Test Set|0.87|\n|Train-Unseen Test Set|0.67|\n\nTurning to the PoseBusters dataset, a comprehensive sequence identity analysis was conducted by the dataset creators. They further categorized the dataset into three subsets based on sequence identity: 0-0.3, 0.3-0.95, and 0.95-1.0, containing 129, 111, and 188 data points, respectively. \n\nThe subsequent table presents a comparative analysis of docking performance across these subsets for various baseline methods. Notably, DeltaDock consistently outperforms other methods across all three subsets, demonstrating robustness against varying levels of sequence similarity.\n\n|Method |% RMSD < 2.0 Å|||% RMSD < 2.0 Å & PB-Valid|||\n|-|-|-|-|-|-|-|\n||[0,0.3]|(0.3,0.95]|(0.95,1]|[0,0.3]|(0.3,0.95]|(0.95,1]|\n|EquiBind|0|2|5|0|0|0|\n|TankBind|2|12|26|0|1|5|\n|DiffDock|16|42|51|1|12|24|\n|DSDP|38|45|56|38|43|55|\n|Vina|39|50|44|38|50|43|\n|Smina|**53**|51|48|**52**|51|47|\n|DeltaDock|47|**54**|**66**|47|**53**|**65**|\n\n> **For Weaknesses 2**\n\nThank you for your valuable suggestions. Here, we update the baselines on the PoseBusters dataset(Fig2b) to be the same as the baselines on the PDBbind dataset (Fig2a). Below, we list the docking success rate (% of RMSD < 2.0 Å) of these baselines on both datasets. We have updated this figure in the PDF we submitted.\n\n|Dataset|EquiBind|TankBind|DiffDock|Vina|Smina|DSDP|DeltaDock|\n|-|-|-|-|-|-|-|-|\n|PDBbind|5.5|23.4|38.2|45.0|46.0|51.6|**56.5**|\n|PoseBuster|2.6|15.0|38.0|43.9|50.4|47.0|**57.0**|\n|PoseBuster(PB-Valid)|0.0|2.6|14.0|43.2|49.8|46.0|**56.0**|\n\n\n> **For Weaknesses 3**\n\nThank you for your valuable question. In this work, we prioritized efficiency and therefore employed DSDP for pose initialization due to its speed. While other docking methods like VINA and SMINA can provide accurate results, they require significantly more computational time.\n\nTo address your point, we conducted additional experiments using VINA and SMINA for initialization. It's important to note that our model was trained on DSDP-generated structures.  Even without specific training on VINA/SMINA output, DeltaDock demonstrates an ability to consistently improves performance across all initialization methods, highlighting its effectiveness in refining poses regardless of the initial docking method used. \n\n|Methods|% RMSD < 2.0 Å| | |% RMSD < 2.0 Å & PB-Valid| | |\n|:----|:----|:----|:----|:----|:----|:----|\n| |[0,0.3]|(0.3,0.95]|(0.95,1]|[0,0.3]|(0.3,0.95]|(0.95,1]|\n|DSDP| 38| 45| 56| 38| 43| 55|\n|DSDP+DeltaDock Refinement| 47 | 54 | **66** | 47 | 53 | **65**|\n|Smina| 53| 51| 48| 52| 51| 47|\n|Smina+DeltaDock Refinement| **54**| **59**| 49| **53**| **59**| 46|\n|Vina| 39| 50| 44| 38| 50| 43|\n|Vina+DeltaDock Refinement| 41| 54| 51| 40| 54| 48|\n\n> **For Weaknesses 4**\n\nThank you for raising this important point. To assess the performance gains achieved by combining Vina with DeltaDock's pocket prediction, we conducted blind docking experiments using the PDBbind dataset. The results, summarized in the table below, clearly demonstrate that incorporating DeltaDock's CPLA pocket prediction module significantly enhances Vina's blind docking accuracy.\n\n|Methods|Time Split| | | |Timesplit Unseen| | | |\n|:----|:----|:----|:----|:----|:----|:----|:----|:----|\n| |% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|% RMSD < 2 Å |% RMSD < 5 Å |% Centroid < 2 Å|% Centroid < 5 Å|\n|Vina| 10| 36| 32| 55| 8| 26| 24| 42|\n|P2rank+Vina|30|46|50|66|22|35|40|52|\n|CPLA+Vina|**35**|**55**|**61**|**80**|**28**|**46**|**55**|**76**|\n\nAs for where the improvement comes from, we performed a series of in-depth analyses. This included ablation studies, discussed in Section 4.5.2, and additional experiments presented earlier.  Our findings consistently indicate that both the pocket prediction module and the refinement module contribute significantly to the observed performance. \n\n> **For Questions 1**\n\nThank you for your valuable Question. For the ligand graph $\\mathcal{G}^\\mathcal{L}$ and protein atomic graph $\\mathcal{G}^\\mathcal{P}$, the distance threshold $cut^{\\mathcal{L}}$ is setted as 5.0 Å. For the protein residue graph $\\mathcal{G}^{\\mathcal{P}\\*}$, the distance threshold $cut^{\\mathcal{P}*}$ is setted as 30.0 Å.\nWhen constructing the protein residue graph, we follow the EquiDock[1] to build the K-NN (k=10) graph.\n\n[1]. Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking, ICLR 2022.\n\n---\nOnce again, thank you for reviewing our paper and providing valuable suggestions, please let us know if you have any further concerns, and we are willing to answer any further questions you have about our paper.\n\nBest regards,\n\nPaper Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear Reviewers,

Thanks again for your insightful comments and valuable suggestions, which are of great help to improve our work. 

In the appended PDF file, we present additional experiments conducted in accordance with the reviewers' suggestions. These experiments aim to further strengthen our findings and address your valuable insights. 

We sincerely hope that our responses effectively address your concerns. If not, please let us know your further concerns, and we will continue actively responding to your comments and improving our paper. We are looking forward to your further responses and comments.

Best regards,

Paper Authors

Meta-review: The paper propose a new AI docking method. Briefly, the method can be decomposed into three steps: 

1. pocket prediction; 

2. docking with an existing tool and in this paper, DSDP; 

3. refinement.

The method itself is a valuable attempt for the research community. Two additional study should be included in the next version:  (i) the effectiveness of pocket prediction; trying the pocket prediction method used in FAbind  (ii) a table showing the performance of refinement (by redocking or fixing the pocket prediction).
Decision: Accept (poster)