Group Ligands Docking to Protein Pockets
Authors: Jiaqi Guan
Jiahan Li
Xiangxin Zhou
Xingang Peng
Sheng Wang
Yunan Luo
Jian Peng
Jianzhu Ma
Venue: ICLR 2025 Poster (2025)
Keywords: molecular docking
ai4science
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose \textsc{GroupBind}, a novel molecular docking framework that simultaneously considers multiple ligands docking to a protein. This is achieved by introducing an interaction layer for the group of ligands and a triangle attention module for embedding protein-ligand and group-ligand pairs. By integrating our approach with diffusion based docking model, we set a new state-of-the-art performance on the PDBBind blind docking benchmark, demonstrating the effectiveness of our paradigm in enhancing molecular docking accuracy.

Published as a conference paper at ICLR 2025
GROUP LIGANDS DOCKING TO PROTEIN POCKETS
Jiaqi ; Jiahan Jiahan Li,; Xiangxin Zhou², Xingang Peng',
Sheng Wang", Yunan Luo Jian Peng", Jianzhu Ma²
1 University of Illinois Tibana-Champaign 2 TTsnnguu University,
8 University of Chinese Academy of Sciences, 4 Peking University,
University of Washington, 6 Georgia Institute of Technology. Helixon Research
j aqii @ 111 llinoiii edu, majianzhuête nighaa cn
ABSTRACT
Molecular docking is key task in computational biology that has attracted in- creasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose GROUPBIND, a novel molecu- lar docking framework that simultancously considers multiple ligands docking to a protein. This is achieved by introducing an interaction layer for the group of ligands and triangle attention module for embedding protein-ligand and group- ligand pairs. By integrating our approach with diffusion-based docking model, we set new S performance on the PDBBind blind docking benchmark, demonstrat- ing the effectiveness our proposed molecular docking paradigm.
INTRODUCTION
The 3D binding structures of molecular ligands and protein pockets are not only fundamental to un- raveling the intricate protein-ligand interactions but also play a pivotal role in rational drug design. In this context, the molecular docking task, which involves predicting the optimal binding config- uration between a ligand and protein, stands cornerstone in modern computational biology. Conventional computational approaches leveraging the pre-defined score functions to search or op- timize a Low-energy pose configuration (Halgren et al. 2004 Trott & Olson 2010) usually slow and inaccurate, which limits their applicability in real- world drug discovery scenarios. In recent
years. deep learning- bassed algorithms (Stärk et al. 2022 Lu et al. 2022; Zhang et al. [022] Corso
et al. 2022) have showcased remarkable success in accurately predicting the protein-ligan binding structures, which brings new breakthroughs in the field of molecular docking.
While these deep learning methods vary in modeling architectures and objectives, they all treat each protein-ligand pair individually. Given the limited number of well-studied protein targets compared to the vast space of molecules, many ligands share common protein targets in available databases. Exploiting the correlated information among these complexes could enhance docking performance.
In this work, we leverage a key biochemical insight: if multiple ligands can bind the same protein pocket, their docked 3D structures are likely to be similar (Paggi et al. 202T]. For instance, Figure [ shows four ligands bind- ing to the same target protein (UniProt ID: B BIMDI3), all exhibiting very similar docking poses. This similarity arises from the conserved interactions within the binding site, suggesting that the protein pocket maintains con- sistent set of key interactions that facilitate binding. For example, ligands often form similar hydrogen bonds with specific protein atoms, resulting in low-energy complexes and analogous spatial arrangements when docked to the same protein.
Figure 1: Multiple ligands that can bind the same protein pocket exhibit simi- lar docking poses.
"Equal Contribution

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To harness the information shared among ligands binding to the same protein pocket, we propose GROUPBIND, a novel molecular docking paradigm that considers multiple ligands docking simul- tancously. When docking ligand A to a protein pocket P. same binding group to enhance docking performance of A to P, as they may share similar poses and key interaction patterns. In other words, we can dock group of ligands the same protein pockets at the same time. Specifically, we develop an interaction layer that enables message passing among group ligands and a triangle attention module for pair representations, based on interaction consistency between different ligands and the protein pocket. We also encode specific structural in- formation into these representations by providing additional supervised signals during training. By integrating our approach into diffusion- docking model (Corso et al., 2022), we achieve new state-of-the-art docking performance on the PDBBind benchmark.
can utilize other ligands in the
To summarize, we highlight our contributions as follows: (1) We propose novel paradigm for molecular docking by considering multiple ligands docking to the protein pocket based on a physical intuition. (2) We develop a new model, comprising modules which capture protein-ligand interaction consistency. This is the first time the concept that multiple molecules should yield similar docking poses has been introduced into end-to-end deep neural network approach to the molecular dock- the new paradigm mentioned above, for the first time verifying the feasibility of the idea that using multiple similar molecules to dock into a protein pocket simultaneously can enhance the docking accuracy.
ing task. (3) We provide a specific algorithmic implementation
RELATED WORK
Molecular Docking. Molecular docking aims at predicting the binding structure of a small molecule ligand to protein, which is a critical technique in drug discovery and enables understanding of the intermolecular interactions and potential binding modes (Ferreira ct al. (0155). Traditional molecular docking approaches usually design scoring functions and then adopt search, sampling, or optimiza-
tion algorithms (Shoichet et al. 1992 Meng et al., 1992 Trott & Olson 2010). The integration of deep learning techniques has
advancements in enhancing scoring functions (Ragoza al.)
2017 McNutt et al.] 2021 Méndez-Lucio et al. [022]. Recently, with the development of geometric deep learning, direct prediction of the binding pose has become an emerging paradigm and achieved promising results. As representative examples, TANKBind (Lu et al. (022) and Uni-Mol (Zhou) al., 2023) predict protein-ligand distance map and using post-optimization algorithms recover the pose from the predicted distance map, while EquiBind (Stärk et al., 2022) and E3Bind (Zhang al. 2023) directly predict the coordinates of the ligand binding pose. FABind (Pei et al.) 2024) and PAbind((Gaa et al.) 2024) enhance docking accuracy by directly introducing a pocket predic- tion module. Among these, Corso et al. (2022) framed molecular docking as a generative modeling problem and proposed DifkDock, diffusion model (Ho et al. 2020 Song al. (02T) that learns the distribution over the product space of the ligand's 's translation, rotation, and torsion, along with confidence model to pick up the final prediction from sampled ligand poses. Although recent physics-based docking methods (Paggi et al.) 2021 McNutt & Koes| 2024) have utilized similar binding molecules poses to enhance docking accuracy, our proposed GroupBind is the first deep learning-base based method capable of integrating with existing docking frameworks and improving per- formance by leveraging group binding data.
Consistency in Data. Consistency is a common nature of biological data. This essential feature provides us insights to better understand function, structure, and evolution in biology. Conserved sequences, maintained by natural selection, are identical or similar sequences in nucleic acids or pro- teins across species, and many multiple sequence alignment (MSA) algorithms, such as ProbCons (Do et al. (2005) and CONTRAlign (Do et al., 2006), are developed to reveal such patterns. Liao al. ( Hashemifar et al. (2016) developed effective protein-protedl interaction (PPI) network alignment algorithms, which help identify conserved subnetwork and thus enable accurate identi- fication of functional orthologs across species. Consistency also exists in other domains beyond biology and such methodology of mining consistency from data has also been extensively studied, e.g., network alignment (i.e., finding the node correspondence across different networks) which is of great value in social networks analysis and web mining (Zhang & Tong 2016 Zhang et al.) 2021).
Depiistency-Inspireee Models. By virtue of its ubiquity and usefulness, consistency serves as a prior knowledge that plays erucial roles in various areas. Xiao & Quan (2099) and Hu et al. (202))
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leveraged the visual consistency among different views of the same object improve the semantic segmentation and 3D shape completion. respectively. Zheng et al. (202]) proposed consistency regu- larization for cross- moss-lingual fine-ture tinettuning inspired by the beliet that the answers of semantically similar questions are supposed to be consistent. [Zhou et al. (2003) introduced the assumption prior that samples in the same cluster tend to have the same labels to semi-supervised learning. Consistency also exists in the protein-ligand docking task: different ligands tend to form similar interactions with the protein. Inspired by this intuition, we propose new module capturing such protein-ligand interaction consistency.
METHOD
In this section, we present GROUPBIND, a new and effective paradigm that leverages other ligands binding to the same protein pocket to improve single ligand docking accuracy, where we dock group ligands to this pocket the same time. Section 3.1. we first define notations and briefly recap the diffusion model for molecular docking, which we will integrate our paradigm into as an example in this work. Then, we introduce the modeling objective of GROUPBIND Section 3.2 In Section 3.3 illustrate how we parameterize GROUPBIND with message passing across group ligands and triangle attention between group ligands and proteins. Finally, in Section 3.4, we describe the auxiliary distance prediction during training and the confidence model for group ligands.
(a)
(b)
Gr
Trangle Attention
Z/L
Gr
Query
May
X:
Ligands Database (known to bind the target protein)
Gisa
Gra
:
Geoug Ergges
Proten-Ligand Eopus
(c)
hm
du
Ard
ht
Sgt
Outpots
Amupline Module
<ip
Corston
Triangle Attention
by
Figure 2: Overview of the proposed GroupBind paradigm. (a) GroupBind leverages other ligands 9L known to bind the same target protein 9r- to facilitate the docking of 9Le- (b) GroupBind introduces the message passing across group ligands and the triangle attention module between group ligands and proteins. (c) GroupBind is not limited to any specific docking framework and can be integrated into existing models (dashed gray box) with the aforementioned modules (blue ones).
3.1 PRELIMINARIES
Problem Definition and Notation. In the molecular docking task, we are provided with a protein (pocket) with Np residues and ligand with n atoms. Our goal is find the docking pose E 3 of the ligand in this binding site. Following previous work (Lu et al. 2022 Corso et al., 2022], we represent protein pocket as residue-level proximity 3D graph Gr (vr, Er). where Vp denotes the residue vertex set consisting of residue vertex features Vp E R NeXN. (such as amino acid types) and C, atom positions of residues Xp Nr and Ep denotes the edge set which is built upon residue 3D proximity. Similarly, the small molecule ligand is also represented as a 3D graph GL = (VL, } Since bond lengths, angles, and small rings in the ligand can be regarded rigid structures, the torsion angles almost determine the flexibility of ligand conformations Thus, we can also define the space of ligand poses on a submanifold Ma E mm- instead of 3m given the seed conformation (Corso et al. 2022), where is the number of rotatable bonds in the
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ligand and the six additional degrees of freedom correspond to the roto-translation relative to the protein pocket.
Diffusson Model for Molecular Docking. Recently, DiffDock (Corso et al. 2022 proposed to formulate molecular docking as a generative task, with the objective of modeling the conditional distribution p(g]Gr, G1) and g .Me. For given seed conformation c, we can define bijection P P- where P = T3) x SO(3) x SO(2) is the production space of ligand translation r, ro- tation Rt and torsion angles 0. Leveraging recent progress of diffusion models on Riemannian man- ifolds (De Bortoli et al. 2022), we can define the forward diffusion independently in each manifold, resulting direct-sum tangent space: T.P T,Tx @ TR))(3) @ (2SO(2) = R R + "".. Based on this formulation, we can train the diffusion model by score matching on translational scores, rotational scores and torsional scores, and sampling ligand poses by running the reverse diffusion process (Song et al. )021).
3.2 MODELING GROUP LIGANDS
Existing deep learning-basee based models for molecular docking consider each protein- -igannlindd pair indi- vidually. However, one important biological observation suggests that when multiple ligands capable of binding to a common protein pocket, the resulting docking poses are expected to exhibit significant degree of similarity. To include this inductive bias, we propose to model the docking poses of a group of ligands given the common protein pocket, instead of modeling them individu- ally. Specifically, we are docking a ligand GLo along with & other ligands of interest .r..... ,Lx to a shared protein pocket Gp. The K other ligands GLIx could be the ones whose docking poses we are also interested in, or retrieved from some database as the augmented ligands to help us dock OL.- Importantly, we do not utilize the binding structural information of augmented ligands GL during the inference phase, but only leverage these ligands known to bind to the same protein to im- prove single protein-ligand docking performance. Under this formulation, our modeling objective becomes the product space P. of translation, rotation and torsion angles this group of ligands, i.c.
p ((rox, Ro.k, Go.k M9r, GLoxx)
(1)
where P., = ((3) x (0(3)((+) 80(2)_ m.y
Considering the effectiveness of generative models in molecular docking, such as diffusion models (Corso al. 2022). we choose to adopt a diffusion framework model the binding distribution. However, our modules can be integrated into other frameworks that use pairwise representations of molecules. The key technical challenge lies in enabling each ligand to become aware of other ligands in the group to improve docking performance. In the following, we will explain how we address this by constructing graphs among group ligands and introducing novel network architectures that allow the model to account for interactions within the group
3.3 PARAMETERIZZTTION OF GROUPBIND
Construct Ligand Group. Ligands are grouped together when they share the same protein binding pocket, as outlined in Algorithm I in Appendix. We group complexes based the protein's 's UniProt ID and name provided in the original PDBBind dataset. To align proteins within the same group, we use the Kabsch algorithm (Kabsch 1976) compute the optimal roto-translation based on the longest common subsequence of amino acids. The reference protein is chosen as the one with the lowest average not-mean-square deviation (RMSD) from others in the group. The resulting optimal rigid transformations are applied to both the protein and ligand in other complexes. To ensure high- quality training data, we also filter out complexes with grotein-ligane minimum distances that are either too close or too far.
Build Group Ligands Graph. To facilitate the exchange and aggregation of useful docking infor- mation, we construct graph across ligands in the group by placing all group ligands in the same coordinate system and adding edges between different ligands. This design facilitates the exchange and aggregation of useful docking information among ligands.. A straightforward approach to con- structing the group ligands graph is to dynamically build a radius graph or k-NN graph based on the diffused group ligands, which are the ligands with added noise during the diffusion process. This is similar to how the protein-ligand heterogeneous graph is constructed. However, in scenarios where the noise level is high, the edges formed based on 3ID proximity relative to the protein may
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inadvertently link two atoms in different ligands with entirely distinct interactions with the protein pocket. In such cases, the message passing process among group ligands might not yield helpful insights and, in fact, could have adverse effects. Conversely, considering the computational resource constraints, creating a fully-connected graph for group ligands and discerning which connections pertinent from the data also impractical.
Given that ligands binding to the same protein pocket typically exhibit some degree of structural similarity, we propose an approach construct the group ligands graph that is independent of the protein pocket. This independence implies that the construction of the group ligands graph is solely based on the structural and 3D geometry similarities among ligands. To achieve this, we initially conduct 2D molecular graph matching, establishing a one-to-one mapping through the Maximum Common Substructure (MCS) (Raymond Willet 2002). For those unmatched atoms, we build edges by connecting their neighbors within 4A if these ligands are presented as training data. Other- wise, i.e. in the inference phase, we perform 3D matching (Wildman & Crippen 1999) on generated molecular conformers and similarly connect neighbors within 4A radius. The edges among group ligands are independent of the diffusion time, and since the C-C bond length is about 1.5Ā. The 4A is chosen by assuming each atom interact with corresponding 2-hop neighbors.
Message Passing across Group Ligands. To make group ligands aware of each other, we introduce message passing mechanism across group ligands based on the aforementioned group ligands graph. Similar to DifiDock, we utilize irreducible representations (irreps) of 50(3) (Thomas et al.) 2018 Fuchs et al. 2020) to construct the message passing layer, implemented with the e3n library GGegger & Smidt 2022). Denoting the group ligands graph as A, the node features as hy and h;, where J, represent atoms from two different ligands, with k being in the neighborhood of within the group ligands graph, i.e. {i E VL, k E VL,.1 9 # g', (i. k) E Ea). the message passing layer for each ligand atom j within group ligands at E-th layer is then defined as follows:
Aah = BN
1
Σ kENA()
((fr)) Baig h (0)
S4 = (1h) (1) h ejn)),
where BN indicates the batch normalization layer, (rrx)) are the spherical harmonics of edge vector Tyk up to / = 2. and By denotes the spherical tensor product with path weights dhyk, which is obtained through a MLP o, taking input the edge embedding еÿж and scalar node features. The
node feature update Aahl" (e) will be combined with other updates derived from the base docking
neural network to obtain the updated the ligand node feature h, (e+1) as shown in Figure 2
Triangle Attention Between Group Ligands and Protein. While the above message passing mechanism establishes connections between different ligands within the group, it lacks consider- ation for the interaction consistency with the protein. Inspired by AlphaFold2 (Jumper al.) 2021). we introduce triangle attention module upon pair embeddings to incorporate geometric consis- tency. It is worth noting that Lu et al. (2022): Zhang et al. (2022) also employ triangle attention modules to enforce geometric constraints. However, key distinction lies in our attention module operates across different ligands, reflecting a fundamentally different underlying motivation: atom j in ligand Ly can interact with amino acid i, then the corresponding atom k in similar ligand Lg should also interact with amino acid i.
To incorporate this idea, we first construct pair representations Zy for protein-ligand edges, and gjk for group ligand-ligand edges, which are both obtained from MLPs with scalar node features and edge embeddings:
(f) (ch (8) -n.2 DD.
(6)
o (b n c,xl).
gyk
When ligand atoms j and exhibit structural similarity, it is expected that they would engage in similar interactions with any protein atom i. To capture this, implement triangle multi-head
As we will see below, the computational cost of triangle attention is O(Epst x |E where [Epd] is the number of edges between protein and ligands, and |Eg is the number of edges between group ligands. A fully-connected graph will induce large number of [Egil and thus high computational cost.
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attention mechanism involving atoms i.j, k to update pair features Zij. The attention weight is controlled by Eyk: leading to the following formulation in Equation
is is
+Lnner concata
(E) (Pth Σ
Pigke
iik
((a)²"))
(E)A
(F)A Ki + (1)A))
(4) where Qy: Kak, Vik are linear transformations of protein-ligant pair embedding z, and by is the linear transformation of group ligand pair embedding Ekj. is gated linear transformation and the dimensionality of the query / key value features.
softmax
Similarly, exchange the roles of key embedding ka and attention bias buj. resulting in a new attention module with group pair embedding as attention keys, which stacked with the aforementioned attention module to improve the model capacity:
(1) i
(8) Linear concatA
Σ
(0t (e Sigt
((aigh)) )))
Zij
Vis
( Pijk
softmax
1
(0) Pig
T 10th
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+ 6
(S)
Finally, we update the pairwise protein- ligand edge features with the pair representation 2 and the initial edge embedding eb through MLPs:
(e+1) 4
02(ee 01 (2,)))
(6)
In this way, we introduce a pairwise protein-ligand edge hidden variable c, which is updated through layers, instead of regarding them as fixed edge embeddings. This allows effective message passing between protein and group ligands through layers.
(0) (1 0, .,... ....,L L 1)
3.4 TRAINING AND INFERENCE
Auxiliary Distance Prediction. By constructing Ctj as hidden variable, we can encode more structural information into it, not merely using it an edge feature. We propose to predict the binned pootiinlligand distances from the pair representations during the training phase, i.c. di Φd ( It provides the supervised signal to make pairwise features encode specific geometric information. Since this work, we integrate GroupBind with the DiffDock framework, the auxiliary training loss becomes weighted cross entropy loss based on the diffusion time t:
B
Ldist
log d
Npains
where B is the total number of distance bins.
Confidence Model. Following DifiDock, we also train a confidence model to perform ranking on sampled ligand poses. We run the trained diffusion model to collect set of candidate poses for each target protein and generate the binary label by checking whether the ligand RMSD is less than 2 A. Notably, unlike DiffDock,, our confidence model involves ranking a group of ligand poses that can bind to the same protein. the preliminary experiments, we observed that training the confidence model to directly predict the group score did not yield optimal results. Instead, training the confidence model to predict individual scores and subsequently computing the group score by averaging them proved more effective, i.e. the group confidence is
c(Xu. ... xK) K
(xx)
where d. is the confidence model, and we rank ligand poses (Xp..... xx) as a group by the group confidence cf c(xa.....
.. xx) instead of ranking them individually.

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Network Implementation. Following TANKBind Lu et al. (2022). we also apply the ML-based ligand binding sites prediction algorithm P2Rank Krivák & Hoksza (2018) to segment the protein into potential binding pockets, which is mainly due to computational memory considerations. Ad- ditionally, in most real application scenarios, the ligand binding site is known. We set the pocket radius 20 A, which is large enough to enclose the ligand molecules in most cases. The denoising network consists of embedding layers, interaction layers and output layers following DifiDock, and each interaction layer is combined with our proposed group-ligand interaction layer and triangle attention layer between group ligands and protein. Since our proposed modules introduce addi- tional parameters, we use five interaction layers instead of six, resulting in 18.8 million trainable parameters, which is par with 20.3 million parameters of DiffDDck..
Training Consideration. During the training phase, we set the maximum number of ligands in the group (in which the ligands can bind to the same target protein) as 5. If there are more than five ligands binding to the same protein, we perform the agglomerative clustering [efays] Defays]([977) (1977) on ligands based on their Tanimoto similarity [Bjjuzz et al. (2015) to make sure each group has less than or equal to ligands. We select ligands whose center of mass is less than Á from the center of native pocket or P2Rank predict pockets training data.
Inference Procedure. During inference, we do not rely on 3D structural data of the ligands. Instead, leverage 2D representations of other ligands known to bind to the same protein to improve the docking performance of the query ligand. Given a protein and query ligand, we search the database (in our case, the PDBBind dataset) identify ligands that bind to the same protein. If such ligands are found, they are incorporated into the docking process alongside the query ligand. The resulting docking pose of the query ligand is then used the final output. If such ligands are available, our approach defaults to the standard single-ligand docking settings, as explored in prior studies.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Dataset. We evaluate our method PDBBind v2020 dataset (Liu et al. 2015). which contains structures of 19443 protein-ligand complexes collected from PDB (Berman et al. 2003). We use the same processed proteins Stärk et al. (002)), which uses reducef to correct and add miss- ing receptor hydrogens. We use the same time-based split following previous work (Stärk et al.] 2022 Lu et al. 2022 Corso al. 2022), resulting in 17k complexes from 2018 or earlier for train- ing val/vatitt and 363 complexes from 2019 with no ligand overlap for testing.
Baselines. We compare our model with search-based methods SMINA Koes al. (013), GN- INA McNutt et al. (202I), and various state-of-the-art deep learning methods EquiBind Stärk et al. (2022). TANKBind Lu et al. (2022) and DifiDock Corso al. (2022). For searching- based methods SMINA and GNINA, we also test their variants by combining them with P2Rank or EquiBind to identify the initial binding site first.
GroupBind. We test the performance of GroupBind under different settings, denoted as different postfixes: 1. Ref: Utilizes the native protein pocket, where the pocket center defined as the centroid of the centers of mass (CoMs) of the group ligands. 2. P2R: Uses the top-3 P2Rank predicted pockets. For each pocket, we sample the same number of ligand poses and apply our confidence model to select the top-K ligands across all poses. 3. S(G): Groups ligands within the test set for self-augmeenation, denoted as self-augment variant ("-S"). The 363 test ligands form 86 groups (255 ligands) with more than one ligand in each group, and 108 groups with single ligand. 4. AG): Expands the augmented ligand database using ligands from the training set, allowing for the augmentation of 32 out of the 108 single ligands. 5. N(G): No augmented ligand database is used, only the original ligands are utilized for docking.
4.2 IMPROVING DOCKING PERFORMANCE WITH GROUPBIND
Following prior work Stärk et al. 2022); Lu et al. 2022) Corso et al. 2022 in assessing the quality of the generated complexes, we calculate the permutation symmetry corrected Meli Biggin (2020)
https:://githbb..omm

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Table Summary of top-1 ligand RMSD and top-5 ligand RMSD metrics of baselines and GROUP- BIND variants. Baseline results are from the original DIFFDOCK paper, except the DIFFDOCK itself, which we perform sampling following the official repository and report the evaluation results. (1) / ()) denotes larger / smaller number is better. The best results are highlighted with bold text (The GROUPBIND-RED variants are not taken into consideration for a fair comparison).
Top-1 Ligand RMSD
Top-5 Ligand RMSD
Percentiles ())
% < thres. (f)
Percentiles (1)
% < thres. (1)
Method
25th
50th
75th
5A
2
25th
50th
75th
5Á
2
GNINA
2.4
7.7
17.9
40.8
22.9
1.6
4.5
11.8
52.8
29.3
SMINA
3.1
7.1
17.9
38.0
18.7
1.7
4.6
9.7
53.1
29.3
TANKBIND
2.5
4.0
8.5
59.0
20.4
2.1
3.4
6.1
67.5
24.5
SSRANK-SMINA
2.9
16.0
43.0
20.4
4.4
54.8
33.2
P2RANK
1.7
15.9
47.8
3.4
12.5
60.3
38.3
EQUIBIND+SMINA
2.4
6.5
11.2
43.6
23.2
1.3
3.4
8.1
60.6
38.6
EQUIBIND+GNINA
1.8
4.9
13.0
50.3
28.8
1.4
3.1
9.1
61.7
39.1
DIH+DOCK
1.6
3.6
7.9
59.3
32.4
1.4
2.4
4.8
75.6
41.3
GROUPBIND-P2R-S
1.7
3.4
7.9
57.9
2.1
77.8
47.4
PRO2PB
1.8
3.2
6.6
66.2
32.1
1.3
2.1
4.0
80.1
48.8
GROUPBIND-RefE
1.6
2.8
5.8
69.3
36.6
1.2
2.0
3.9
82.3
50.7
GROUPBIND-RetE
2.9
5.9
70.4
36.3
1.2
2.0
84.5
49.0
PD3211
QBH7266
Reference
GroupBlnd
Bepret 2,00
Repi:
Goiz: 0.68
Boir: 0.47
DifDook
depi: 3.54
totg:
Beppe 1.50
Figure 3: Visualization of two examples (UniProt ID: P03211, Q9H7Z6) with reference ligand docking poses, GroupBind's 's best predicted docking poses and DifiDock's 's best predicted docking poses. There two and three different ligands that can bind to P03211 I and Q9H7Z6. respectively, highlighted in distinct colors. Reference ligands tend to bind the target protein with similar poses, which is well captured with GroupBind and results in lower average RMSD compared to DiffDock.
heavy-atom RMSD between predicted and crystal ligand atoms. We report both 25th / 50th / 75th ligand RMSD percentiles and the percentages of predictions with an RMSD less than 2 Á or 5 A.
All of baselines and GroupBind variants are able to generate multiple structures and rank them, and sample 40 poses per pocket as the same setting of DifiDock. Results of the centroid distance can be found in the Appendix Section D We also provide examples of docking poses generated by GroupBind compared DiffDock in Figure
We report the top-ranked prediction (Top-1) and the most accurate pose among the five highest ranked predictions (Top-5) in Table [ can be seen that GROUPBIND-P2RANE ANK, the models in the full blind docking setting and thus fair to other baselines, can outperform all previous methods with clear margin, especially top-5 ligand RMSD. The self-augment variant ("-S") and training- augment variant ("-A") can achieve 47. 44% and 48.8% with RMSD < 2 Á. We analyze the effects of
using augmented ligand molecules comprehensively in Section 4.3 When provided with the native reference pocket, the t ligand RMSD is significantly improved to 36.6% and 36.3% respec- tively. These results indicate that GroupBind can improve the docking performance by effectively leveraging the fact that multiple ligands tend to bind the same target protein in similar poses.
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60
(86)
55
Frection RMSD 2A 196
59
AG
58
with
45-
GroupOind-
40-
Group6ind- Ref-A
GroupGind-
55
Group6ind- P2Rnnn-
54
DifDDok
10
20
Ligands Group
(a) The Docking Success vs. Top-K selection
(b) Effect of Group Size
Spearman r=0.39
NG
Fractoon with RMSD (Sa
70
SG
60-
Best RMSD
50-
40
0.2
0.4
0.6
1.0
5G
Wi AG
w/l G
Tanimoto Similanty
Subset of Test set
(c) Efffect of Group Similarity
(d) Bifectt Different Subsets
Figure 4: Analysis of the effect of augmented ligands. (a) The docking success rate (ligand RMSD
2 A) increases with the number of selection increases. (b) Effect of the group size. "SG" and "AG" indicate group ligands within test set and further utilize ligands from the training set respectively. (c) Effect of Group Similarity. X-axis denotes the Tanimoto similarity within the ligand group and Y-axis denotes the average best docking RMSD. (d) Effect on Different Subsets. "w/SG w/AG and w/o G indicates the subsets where augmented ligands from the test set, the training set, and no augmented ligands can be used respectively. "SG, AG, NG" indicates the corresponding model performance under different augmented ligands settings: test set, training set and no augmented ligands. For (b)(c)d)), the best docking pose among 40 candidates is applied for the success rate.
Taking a closer look at the top- K fraction with ligand RMSD < 2 Å, we can see that our proposed GroupBind is consistently better than DiffDock for K 1 and the margin becomes larger as K increases, shown in Figure 4a With the perfect selection (equivalent to K 40). GroupBind can achieve around 55% 60% success rate while DiffDock can only achieve 47%. This indicates
our model has great potential generate high-quality binding pose. Developing more powerful confidence model is one promising direction to improve the docking performance further.
4.3 EFFECT OF AUGMENTED LIGANDS
We train GroupBind with the maximum number of ligands in each group 5. However, we can assign a different maximum number of ligands during the sampling phase. In Figure (4b we in- vestigate the influence of using different numbers of augmented ligands in sampling. Similarly, report the perfect selection results to isolate the influence of the confidence model. We can see that with augmented ligands, the success rate clearly improves, from the test set itself (SG) or the whole dataset (AG). The optimal number of augmentation ligands does not strictly match the ones we used during training. Upon closer inspection of the structural similarity within each group, we find a subtle correlation between group similarity and the best ligand RMSD. This suggests that combining dissimilar ligands proves advantageous for our model in discerning correct interactions with proteins, ultimately yielding more accurate docking poses. Consequently, this highlights the pivotal role of the quality of augmentation ligands over their sheer quantity in enhancing the overall performance of our model. In Figure/4c the presence of dissimilar ligands binding to the same pocket provides valuable information to our model, leading to improved docking g accuracy and generally lower RMSD values (Spearman correlation -0.39). This suggests that grouping dissimilar ligands advantageous for our model in discerning correct interactions with proteins, ultimately yielding more accurate docking poses. In Figure 4d split the test set
matter the augmented ligands come

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into several subsets further to investigate where the performance improvement comes. The subsets are: (1) w/ SG, where ligands can be augmented with test set itself (255 PDBs). (2) w/ AG, where ligands can be further augmented by the training/validation set (32 PDBs) and (3) w/o G, where can not find the augmented ligands in the whole dataset (76 PIBB). We can see the docking per- formance can be improved significantly if
apply augmented ligands (w/ SG, w/ AG), while for the complexes where we cannot find other augmented ligands binding to the same protein, the performance remains unchanged and is typically hard predict (only 30% perfect success rate), compared to other complexes (50% 70% success rate).
4.4 ABLATION STUDIES
Table 2: Ablation studies on our proposed modules. "MP-GL" denotes the message passing across group ligands; "Att" denotes the triangle attention module; "Dist" denotes the auxiliary distance prediction. The results of perfect selection under 10 samples are reported.
Method
MP-GL
Att
Dist
% < 2A
Median
DIF+
30.6
3.0
35.2
2.6
GROUPBIND
38.8
2.5
42,9
2.2
To investigate the effectiveness of each component in GroupBind, we perform the ablation study as shown in Table We employ smaller neural network in both DiffDock and GroupBind variants, and the results of perfect selection under 10 samples are reported to isolate the influence of the confidence model. See more experimental setup details and full results in the Appendix Section C It can be seen that each component can improve the docking performance substantially. One surprising thing that even though the introduction of the auxiliary distance loss is straightforward, it can further improve the docking performance dramatically, proving the effectiveness of providing supervised signals to guide the learning of pairwise embedding.
4.5 DOCKING LIGANDS TO NEW PROTEINS
Table 3: Performance comparison between No Augmentation and Augmentation settings.
Top-I Ligand RMSD
Top-5 Ligand RMSD
25th
50th
75th
% < 2Á
% < 5
25th
50th
75th
% < 2Á
% < 5Á
Aug
2.3
4.9
24.9
20.5
50.0
1.5
3.2
6.8
34.1
68.2
Aug
1.8
4.7
15.5
29.6
50.0
1.5
3.4
6.3
38.6
65.9
As our method relies on ligands from the same binding group, we considered cases where no binding information is available for new protein. In such cases, ligands binding homologous proteins can be used as an alternative. To simulate this, we used FoldSeck (Van Kempen et al. 2024) (E-value < le (e-5) 5) identify homologous proteins and collected ligands (Tanimoto similarity 0.5) binding to them for proteins without known ligands in PDBBind. These ligands were used as augmented inputs in our method. If neither ligands from the same protein nor homologous proteins available, the method defaults to a diffusion-
docking model. Results in Table 3 demonstratt improved docking performance, showing our method's flexibility across various scenarios.
5 CONCLUSION
We introduce GROUPBIND for simultaneously docking multiple ligands to protein pockets, a novel paradigm for the molecular docking task grounded in sound physical intuition. We develop a novel model equipped with specialized modules to capture the consistency in -rotein-ligand interactions, holding the potential for seamless integration with diverse deep learning-based docking frameworks. Empirical results demonstrate the efficacy of our paradigm and the augmented group ligands. The limitation of our model lies in the high computation cost of the triangle attention module, render- ing it impractical for constructing a relatively dense graph between group ligands and the protein. Furthermore, the proposed paradigm is contingent on the availability of group ligand binding infor- mation, presenting a limitation in scenarios where such data is absent. One potential future work is to leverage non binding ligand information and construct a model that learns to selectively identify ligands beneficial for the docking process.
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This work supported by the National Natural Science Foundation of China grants 62377030 and China's Village Science and Technology City Key Technology fund- ing.
Acknowledgement
REFERENCES
Dávid Bajusz, Anita Rácz, and Károly Héberger. Why tanimoto index an appropriate choice for fingerprint-bseed similarity calculations? Journal of cheminformatics, 7(1):1-13, 2015.
Helen Berman, Kim Henrick, and Haruki Nakamura. Annonncigg the worldwide protein data bank. Nature structural & molecular biology, 10(12):900-98, 2003.
Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane. Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Sci- ence, 15(9)3313-331) 2024.
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola. Diffdock: Dif- fusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:22 2022.
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet. Riemannian score-based generative modelling. Advances in Neural Information Processing Systems, 35:2406-2422, 2022.
Daniel Defays. An efficient algorithm for complete link method. The computer journal, 20(4):
364-366, 1977.
Chuong B Do, Mahathi SP Mahabhashyam, Michael Brudno, and Serafim Batzoglou. Probcons: Probabilistic consisteney-bait multiple sequence alignment. Genome research, 15()):330-340, 2005.
Chuong B Do. Samuel S Gross, and Serafim Batzoglou. Contralign: discriminative training for protein sequence alignment. In Anmual International Conference on Research in Computational Molecular Biology. pp. 160-174. Springer, 2006.
Leonardo G Ferreira, Ricardo Dos Santos, Glaucius Oliva, and Adriano D Andricopulo. Molecu- lar docking and strueture-based drug design strategies. Molecules, 1084):1338,,1341,, 2015.
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. Se transsfooss::::::: 3d roto- translation equivariant attention networks. Advances in neural information processing systems, 33:1970-1981, 2020.
Kaiyuan Gao, Qizhi Pei, Jinhua Zhu, Tao Qin, Kun He, Tie- Liu, and Lijun Wu. Fabind+: Enhancing molecular docking through improved pocket prediction and pose generation. arXiv preprint 4rXiv:2403.20261, 2024.
Mario Geiger and Tess Smidt. e3nn: Euclidean neural networks. arXiv preprint urXiv:2207.09453. 2022.
Thomas A Halgren, Robert Murphy, Richard A Friesner, Hege Beard, Leah L Frye, W Thomas Pollard, and Jay L Banks. Glide: new approach for rapid, accurate docking and scoring. 2. en- richment factors database screening. Journal of medicinal chemistry, 47(7):1750-1755 2004.
Somaye Hashemifar, Qixing Huang, and Jinbo Xu. Joint alignment of multiple protein-protein interaction networks via convex optimization. Journal of Computational Biology. 23(1 1):903- 911. 2016.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Tao Hu, Zhizhong Han, and Matthias Zwicker. 3d shape completion with multi-view consistent inference. Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 10997-11004, 2020.
11

---

Published as a conference paper at ICLR 2025
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Nunyasuvunakool, Russ Bates, Augustin Zidek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. Nature, 596(7873):5835556
2021.
Wolfgang Kabsch. A solution for the best rotation to relate two sets of vectors. Acta Crystallo- graphica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 32 (5):922-923, 1976.
David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho. Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise. Journal of chemical information and modeling, ((8)):193-1104 2013.
Radoslav Krivák and David Hoksza. P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of cheminformatics, 10:1-12, 2018.
Chung-Shan Shou-SShuuuua Liao, Kanghao Lu, Michael Baym, Rohit Singh, and Bonnie Berger. Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics, 25(12)ii23-i2558 2009.
Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, and Renxiao Wang. Pdb-wide collection of binding data: current status of the pdbbind database. Bioinformatics, (3):0054412, 2015.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.5510,, 2017.
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng. Tankbind: Trigonometry-wvare neural networks for drug-protein binding structure prediction. Advances in neural information processing systems, 35:7236-7249, 2022.
Andrew T McNutt and David R Koes. Open-combind: harnessing unlabeled data for improved binding pose prediction. Biophysical Journal, 123(3):186a, 2024.
Andrew T McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, and David Ryan Koes. Gnina 1.0: molecular docking with deep learn- ing. Journal of cheminformatiis, 13(1):1-20, 2021.
Rocco Meli and Philip Biggin. spyrmsd: symmetry-correater rmsd calculations in python. Journal of Cheminformatics, 12(1):1-7, 2020.
Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona and Jörg Kurt Wegner. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nature Machine Intelligence, 3(12):0333-13)) 2021.
Flaine C Meng, Brian K Shoichet, and Irwin Kuntz. Automated docking with grid-based energy evaluation. Journal of computational chemistry, 13(4):505-52) 1992.
Joseph M Paggi, Julia A Belk, Scott A Hollingsworth, Nicolas Villanueva, Alexander S Powers, Mary Clark, Augustine G Chemparathy, Jonathan E Tynan, Thomas K Lau, Roger K Suna- hara, al. Leveraging nonstructural data predict structures and affinities of protein-ligand complexes. Proceedings of the National Academy of Sciences, (1:5)))c211222)
2021.
Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie. Tao Qin, Kun He, Tie- Yan Liu, and Rui Yan. Fabind: Fast and accurate protein-ligand binding. Advances in Neural Information Processing Systems, 36, 2024.
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo. Jocelyn Sunseri, and David Ryan Koes. Protein- ligand scoring with convolutional neural networks. Journal of chemical information and model- ing, 57(4):942-955 2017.
John W Raymond and Peter Willett. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of computer-aided molecular design, 16:521-533, 2002.
12

---

Published as a conference paper at ICLR 2025
Brian K Shoichet, Irwin D Kuntz, and Dale L Bodian. Molecular docking using shape descriptors. Journal of computational chemistry, (3(3):380-399) 1992.
Yang Song. Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In Interna- tional Conference on Learning Representations, 2021. URL
net/
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. Equibind: Geometric deep learning for drug binding structure prediction. International conference on machine learning, pp. 20503-200 22552-20521. PMLR, 2022.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. Tensor field networks: Rotation-and translationeequivariant neural networks for 3d point clouds. arXiv preprint arXiv:1802.082219 2018.
Robert Tibshirani, Guenther Walther, and Trevor Hastie. Estimating the number of clusters in a data via the gap statistie. Journal of the Royal Statistical Society: Series B (Statistical Methodol- ogy), 63(2):41 4-33, 2001.
Oleg Trott and Arthur Olson. Autodock vina: improving the speed and accuracy of docking with new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2):55-441, 2010.
Michel Van Kempen, Stephanic Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger. Fast and accurate protein struc- ture search with foldseek. Nature biotechnology. 42(2):243-244 2024.
Joe H Ward Jr. Hierarchical grouping optimize an objective function. Journal of the American statistical association, 58(301):236-22 1963.
Scott A Wildman and Gordon M Crippen. Prediction of physicochemical parameters by atomic contributions. Journal of chemical information and computer sciences, 39(5):888 873, 1999.
Jianxiong Xiao and Long Quan. Multiple view semantic segmentation for street view images. In 2009 IEEE 12th international conference on computer vision, pp. 686-693. IEFE, 2009.
Si Zhang and Hanghang Tong. Final: Fast attributed network alignment. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1345- 1354, 2016.
Si Zhang, Hanghang Tong, Long Jin, Yinglong Xia, and Yunsong Guo. Balancing consistency and disparity in network alignment. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp. 2212-2222, 2021.
Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, and Jian Tang. E3bind: An end-to-end cquivariant network for protein-ligam docking. arXiv preprint arXiv.2210.06069 2022.
Yangtian Zhang, Huiyu Cai, Chence Shi, and Jian Tang. E3bind: An end-to-end equivariant network for protein-ligand ligand docking. In The Eleventh International Conference on Learning Representa- tions, 2023. URL
net/ forum? www.iiiii00011ff1000
Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang. Zewen Chi, Saksham Singhal. Wanxiang Che, Ting Liu, Xia Song, and Furu Wei. Consistency regularization for cross-lingual fine-tuning In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3403-341 2021.
Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. Advances in neural information processing systems, 16, 2003,
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: A universal 3d molecular representation learning framework. In The Eleventh International Conference on Learning Representations, 2023. URL https: //openrevien ew. http://rrrmmmm.tkkkk//22/ee..ta
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A CASE MOTIVATION
(a) 6g2c
(b) 6g29
Figure 5: PDB entries 6g2c and 6g29 contain distinct ligands (Tanimoto similarity 0.26) that bind to the same pocket but share similar binding poses.
PDB entries 6g2c and 6g29 contain distinet ligands (Tanimoto similarity 0.26) that bind to the same pocket. Despite their differences, several key interactions are conserved: for instance, hy- drogen bond formed between N3 in 6g2c N12 in 6g29 and oxygen atom in the side chain of pocket residue, while r-stacking interactions occur between the ring C7-Ca-Ng-Cioyy 6g29 and two benzene rings the side chains of pocket residues. These conserved interactions ultimately result in similar binding poses for these distinct ligands.
in 6g2c
/ ring C--NN-NN-Ca-C--44
B DATA PROCESSING DETAILS
We illustrate how we prepare the data for GROUPBIND Section [ After processing the original PDBBind v2020 dataset, we obtain 7323 groups (17649 complexes) for the training set, with 46.3 % groups having more than one ligand. By setting the maximum ligands in each group as we have 8237 datapoints with 51.3 % datapoints with more than one ligand.
In Figure 6a
we present histogram showing the number of ligands per pocket. There are 7697 unique pockets for the 19k complexes in the PDBBind dataset. Among these, 3,681 pockets (47.8%) contain more than one ligand. Additionally, Figure 6bb illustrates the Tanimoto similarity of ligands within each group (with two more ligands). This analysis shows that while some pockets are indeed bonded with similar ligands, a larger portion of pockets contain ligands that are dissimilar.
4000
800
500
3000
300
2000
1500
200
100
500
0.0
0.2
0.4
0.5
0.8
1.0
11
13
15
17
19
Average Tanimoto Similarity
Number Ligands
(b) The Tanimoto similarity of ligands within each group.
(a) The number of ligands per pocket.
Figure 6: Group Ligands Statistics in the PDBBind dataset.
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Algorithm Procedure of preprocessing data for GROUPBIND
Input: A (unaligned) dataset with pooeinn-lgaand complexes {(P(s) _())==
Output: An aligned dataset {(P('), - ) Each datapoint has aligned protein p() and
group of ligands < that can bind to this protein.
1: Group complexes initially by their proteins' UniProt IDs and names, which are provided in the raw PDBBind dataset.
2: Further cluster proteins by aligning their amino acid sequences and then performing agglomer- ative clustering Ward Jr (T963) with the optimal number of clusters, which is determined using silhouette scores Tibshirani et al. (2001).
Align proteins in each group by finding the longest common subsequence of amino acids and apply the Kabsch algorithm Kabsch (9766 to obtain the optimal rototranslation. Compute pairwise alignment RMSDs.
4: Based on the alignment RMSD, select the protein with minimum average RMSD to others as the reference protein. For other proteins, apply to complexes with the solved optimal rigid transformations relative to the reference protein.
5: Cluster grouped complexes further by ligand centers to make sure group ligands are in the same pocket. Repeat alignment steps 3 and 4.
6: Isolate complexes as single groups that have too close (< 0.4 Å) or too far (> 3.0 Å) minimum protein-ligand distances.
C EXPERIMENTAL DETAILS
C.I MODEL DETAILS
We the way to construct the protein-ligand heterogeneous graph as DIFFDOCK and follow the same node and edge featurization to make sure the performance improvement come from our in- troduced GROUPBIND paradigm. The triangle attention module between protein and group ligands is employed at the beginning of each layer, with 4 attention heads and 128-dimension key / query / value hidden features. The model consists of layers with 48 scalar features and 10 vector features, except that in the ablation study Section we use a smaller model with 24 scalar features and 6 vector features.
C.2 TRAINING DETAILS
Since our GROUPBIND operates on the pocket level, we set smaller sigma 10.0 for the translational noise. The other diffusion related parameters remain same as DIFFDOCK. For the auxiliary distance loss (Section 7). we map the distance to 40 bins from 2 Á to 22 Å, and the distance loss is weighted by 0.1 and added the score matching loss. We train our model with AdamW Loshchilov & Hutter (017) optimizer with the learning rate of 0.001 and the weight decay of 10.0.
D ADDITIONAL RESULTS
Following prior work, we also evaluate the top-1 and top-5 centroid distance in Table 4 It can be seen that our models are consistently better than all previous baselines. Augmentation with ligands from the training set also improves the model's performance in terms of centroid distances. We also provide the full results of ablation studies (Section 4.4)) and different numbers of augmented ligands (Section 4.3) in Table 5 and Table 6 for further reference.
E TRAINING AND INFERENCE TIME
We trained our model on eight NVIDIA A100 GPUs for 350 epochs. Each epoch takes about 9min the total training time is about 52.5 hrs. In comparison, DiffDDock is trained on four 48GB RTX A6000 GPUs for 850 epochs (around 18 days). Considering the performance of A100 is about twice that of A6000, the actual training cost for model and DiffDock is approximately 1:2. The
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Table 4: Summary of top-1 and top-5 ligand centroid distance metries of baseline models and GROUPBIND variants. (1) / (L) denotes a larger smaller number better. The best results are highlighted with bold text.
Top-1 Centroid Distance
Top-5 Centroid Distance
Percentiles (U)
% < thres. (1)
Percentiles (U)
% < thres. (1)
Method
25th
50th
75th
5Å
2A
25th
50th
75th
5
2
GNINA
0.8
3.7
23.1
53.6
40.2
0.6
2.0
8.2
66.8
49.7
SMINA
1.0
2.6
16.1
59.8
41.6
0.6
1.9
6.2
72.9
50.8
TANKBIND
0.9
4.4
77.1
55.1
1.4
2.9
SMRANK+
0.8
2.6
14.8
60.1
44.1
0.6
1.8
12.3
66.2
53.4
PIRANK+GNINA
0.6
2.2
14.6
60.9
48.3
0.5
1.4
9.2
69.3
57.3
EQUIBIND+SMINA
0.7
2.1
7.3
69.3
49.2
0.5
1.3
5.1
74.9
58.9
FQUTBIND+GNINA
0.6
1.9
9.9
66.5
50.8
0.5
1.1
5.3
73.7
60.1
DIH+DOCK
0.6
1.4
3.2
80.6
60.4
0.4
0.7
1.8
89.8
78.7
GROUPBIND-P2RANKS
0.5
1.0
3.1
81.2
67.6
0.3
0.6
1.3
91.1
82.3
GROUPBIND-PPRANK-A
0.5
1.0
2.4
83.7
71.5
0.3
0.6
1.2
93.1
84.5
GROUPBIND-Ref'
0.4
1.6
94.5
80.3
0.3
1.1
Aff
0.4
1.5
94.7
84.5
0.3
1.0
97.2
90.6
Table 5: Full results of GROUPBIND ablation studies.
Percentiles (J)
% < thres. (1)
Method
MP-GL
Att
Dist
25th
50th
75th
5Å
2
DIFFDOCK
1.76
2.98
5.21
73.3
30.6
1.70
2.51
4.33
83.9
35.2
GROUPBIND
1.49
2.50
3.98
84.2
38.8
1.39
2.23
4.10
84.2
42.9
Table 6: Full results with different maximum numbers of ligands each group.
Percentiles (L)
% < thres. (1)
Method
# Ligs
25th
50th
75th
5
2
1.09
1.86
3.32
87.5
53.5
2
1.02
1.70
3.20
88.6
57.9
GROUPBIND-Ref-S
3
1.04
1.74
3.19
87.8
55.4
4
1.07
1.73
2.96
89.5
56.5
5
1.03
1.72
3.16
88.9
56.5
2
1.00
1.66
3.18
88.1
56.8
3
0.99
1.71
3.18
89.2
56.2
GROUPBIND-ReF-A
4
1.04
1.6
3.07
90.0
59.6
5
1.02
1.7
2.93
90.0
59.0
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reduced training time for model can be attributed to training at the pocket-level and grouping ligands, which leads to smaller number of training data points compared to DiffDock.
The average time for our method to generate 40 samples per pocket is 30s (with maximum number of ligands in a group set to 5). If we are interested docking poses of multiple different ligands for the same protein, the actual average inference time for each ligand will be lower.
Method
Inference Time (8)
GNINA
127
SMINA
126
TANKBIND
0.7 2.5
P2RANK SMINA
126
P2RANK + GNINA
127
EQUIBIND SMINA
126
EQUIBIND GNINA
127
DiffDock
40
Ours (per pocket)
30
Table 7: Inference time comparison between different docking methods.
F ADDITIONAL EXPERIMENTS
1.0
DifiDock GroupSind
Success rate
0.6
0.4
0.2
0.0
RMSD ZA PB PB-Vald
RMSD s 24
All Atoms connected
Sanitization
Molecuier Formule
Dauble Bond Steranchem
Hotecular Bonds
Tetrahedral Chirality
Bond Lengths
Bord Angles
Internal staric Clest
Aromatic Ring Platness
Double Bond Flatness
Internal Energy
Protein Lig Hor Dist.
Min Dist. Protein
Min Dist. Organic Cof
Min Dist. Inaganicc Col.
Vel Overlap Protein
Voi Overtap Organic Cof
Voi Overlsp Inoraanic Cot
Figure 7: PoseBuster Results
20-
DiffDock
GroupBind
Mean RMSD
15-
40
BLAH
1D6R
BRD4
COR2
DRT
DWPP
ELANE
R20
F2
GurT
Ho9801422
HOURS
NRSC
228491
102700
POEIDE
PLAU
PYP1
MGGH
Figure 8: ComBind Results
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Published as a conference paper at ICLR 2025
We further compare model and DiffDock under the PoseBuster benchmark (Buttenschoen al. 2024) in Figure 7 Our model achieves a higher overall success rate than DifiDock, though with lower PB-Valid success rate. Upon closer investigation, we found that our generated docking poses may contain more instances of unreasonable protein-ligand distances than those generated by DiffDock, which could be side effect of incorporating augmented ligands.
We also included the experiment on the ComBind benchmark (Paggi et al. 2021) Figure As shown, our method consistently outperforms DifiDock across these 30 distinct proteins, benefiting from the insight that multiple ligands binding to the same protein pocket tend to exhibit similar docking poses. However, our model still shows performance gaps compared to ComBind. On one hand, this advantage arises because ComBind defines the docking grid using the center of mass (CoM) of the reference ligand, allowing the method to leverage this information. we attribute this to the more limited generalization ability of DiffDock compared to classic physics- based docking methods like GLIDE, which used ComBind. Combining our framework with more robust backbone model that offers better generalization remains promising direction for future work.
the other hand,
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Reviews:
[{"review_id": "gQO6fxesjV", "reviewer": "Reviewer_Pwff", "summary": "This paper presents a molecular docking framework called GROUPBIND, which enhances the binding capability of a ligand to a target protein pocket by leveraging other ligands that bind to the same pocket. \nThe framework introduces message padding among groups of ligands and a triangle attention module for protein-ligand pairs. Experimental results validate that GROUPBIND improves docking performance based on diffusion models.", "questions": "Refer to the content in the Weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "(1) The writing and organization of this paper are very clear.\n\n(2) This paper is intriguing because it is based on the idea of enhancing the binding capability of the current ligand by considering the binding positions of other ligands that target the same protein.", "weaknesses": "(1) Why are the results of DIFFDOCK in Table 1 worse than those in the original paper, and it seems that the bold text annotations might be inaccurate?\n\n(2) From Figure 1, we can see that molecules binding to the same pocket indeed have similar structures, but how many pockets in the dataset exhibit this situation? Is there any statistical data on the number of pockets and the corresponding similar ligands in the PDBBind dataset? \n\n(3) During inference, when searching the database for ligands similar to the query ligand, how many entries in the test set can retrieve similar ligands? If similar ligands cannot be retrieved, does that mean the model becomes ineffective?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "1ppWG45770", "reviewer": "Reviewer_rEyK", "summary": "This paper introduces GroupBind, a blind rigid docking method predicated on the biochemical observation that ligands binding to the same target protein often adopt similar poses. GroupBind employs an interaction layer for a group of ligands and a triangle attention module to embed protein-ligand and group-ligand pairs. Performance is evaluated on the PDBbind dataset.", "questions": "1.\tThe definition of \"ligand ground graph\" needs explicit formalization. The current lack of clarity makes understanding this crucial concept challenging. For example, when referring “noisy group ligands” in line 214, it is difficult to understand this concept.\n2.\tThe references to tables and figures are confusing. Avoid using \"Section\" to denote figures, tables, and section of manuscript at the same time. Use \"Figure\" when referring to images (e.g., Figure 1, Figure 2) and \"Table\" for tabular data (e.g., Table 1, Table 2). \n3.\tA thorough proofread is necessary to correct spelling and grammatical errors. For instance, \"beyound\" on line 101 should be corrected to \"beyond.\" A comprehensive review of the entire text is recommended.\n4.\tFigure 7's inclusion of Figures 3, 4, 5, and 6. For better visual organization and clarity, these figures should be presented as subfigures within a single figure (e.g., Figure 7a, 7b, 7c, and 7d). This allows for easier comparison and a more streamlined presentation.\n5.\tLacking explanation of \"NG\" in Figure 6. While the meaning of \"NG\" in Figure 6 might be discernible from the main text, its meaning should be explicitly stated. This ensures clarity and avoids requiring readers to search through the text for an explanation. A concise definition or clarification of \"NG\" within the caption is crucial.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of leveraging similar binding poses among ligands targeting the same protein is intriguing and biologically relevant.\n2. The experimental results suggest that incorporating augmented ligands improves docking performance.", "weaknesses": "1. The core idea is similar  to MCS (Maximum Common Substructure) docking [1, 2], which assume that ligands with similar substructures exhibit similar docking poses. GroupBind, however, assumes all ligands share similar docking poses. Figure 1 depicts highly similar ligands with similar docking structures. Conversely, [3] (Figure 4) illustrates cases where structurally distinct ligands adopt distinct poses. A statistical comparison quantifying the difference between the MCS docking assumption and GroupBind's assumption is warranted.\n\n2. Comparing GroupBind-Ref against blind docking methods like DiffDock is unfair. DiffDock performs blind docking, whereas GroupBind-Ref utilizes prior knowledge of the binding pocket, making it a site-specific docking method. This introduces a significant advantage for GroupBind-Ref.\n\n3. The evaluation should include more baselines such as FABind [5] and FABind+ [6], for which source code is available. Expanding the evaluation to include datasets like PoseBuster would assess GroupBind's ability to predict physically plausible structures.\n\n4. The reported top-1 docking success rate for DiffDock (32.4% with 40 samples in Table 1) appears considerably lower than previously \nreported results (38.2% in [4] and 36.0% in [5]). This discrepancy requires clarification.\n\n5. The clarity of the writing could be improved. Specific points of confusion are detailed below in the Questions section.\n\n[1] A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Mod, 2024\n\n[2] FitDock: protein-ligand docking by template fitting, 2022\n\n[3] DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking, 2024\n\n[4] DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking, 2022\n\n[5] FABind: Fast and Accurate Protein-Ligand Binding, 2023\n\n[6] FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation, 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "aMnimZAEeA", "reviewer": "Reviewer_J3p9", "summary": "This paper presents a deep learning model for molecular docking, which improves data utilization and model quality by docking multiple molecules to the same protein. It further enhances the connections between similar atoms across different molecules and the same protein amino acids using a triangular perceptual network. Through this multi-molecule docking approach, the model surpasses existing methods.", "questions": "1.\tThe authors should account for the effect of protein similarity on the results by performing redundancy removal on the test set proteins that are either duplicated or highly similar to those in the training or validation sets. Tools like MMseqs or other alternatives could be used for this process.\n2.\tThe authors need to explain how the similarity between ligands in the group ligand set impacts the results.\n3.\tHow would the results of GROUPBIND change if tested on the latest PoseBuster (version 1 and version 2)?\n4.\tIn Figure 4 on page 8, does the success rate refer to Top 1 or Top 40? What do “SG” and “AG” specifically mean? These details should be clarified in the figure caption.\n5.\tWhat does Figure 5 on page 8 illustrate, and where is it referenced in the article?\n6.\tIn Figure 6 on page 8, does the success rate refer to Top 1 or Top 40? The meanings of “NG”, “SG”, and “AG” should also be clarified in the figure caption.\n7.\tOn line 457 on page 9, the percentage 36.3% is mentioned twice, which could cause confusion and should be clarified.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The study proposes a new molecu lar docking framework to simultaneously consider multiple ligands docking to a protein.", "weaknesses": "Overall, this paper offers a certain level of contribution, but the experimental section requires clearer descriptions and further discussion. For the figures in the paper, the authors should provide detailed captions, including explanations of the methods used.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "D96Qi6YPGC", "reviewer": "Reviewer_fL2Q", "summary": "The paper describes an approach for leveraging the insight that similar ligands that bind to the same protein target are expected to binding similarly.  A method for attending between ligands as part of a diffusion docking process is described and the results convincingly show the benefit of this approach.", "questions": "How does your method compare to ComBind in a head-to-head comparison? Answering this would significantly increase my enthusiasm for the paper as, in addition to providing a more relevant baseline, it would involve testing using ligands without known structure as augmentation ligands (assuming the ComBind evaluation framework is used).", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Particularly during the lead optimization phase, it is reasonable to expect that there are a number of known ligands for the target protein and using this information to improve docking performance could help in the discovery of better ligands.\n\nA reasonable approach for message passing across ligands with triangle attention is described.  This might provide a more general template for other tasks where output are represented as graphs and there is a known consistency bias.\n\nThis approach does not require the accessory ligands to have a known structure at inference time, which is a realistic scenario.\n\nInformative ablation studies are performed.", "weaknesses": "The contributions are inappropriate as written.  This is not the first time the concept that multiple similar molecules can be used to enhance molecular docking has been described.  ComBind (Paggi et al, 2021; cited in the paper) does exactly that.  There are other methods that use this insight in different ways (e.g. selecting poses from ensemble docking).  The contributions should be qualified that this is the first end-to-end deep neural network approach to molecular docking that uses this insight.\n\nThe results only compare to single-ligand docking.  Comparing to ComBind (or OpenComBind if lacking a Schroedinger license) would be more relevant.  Can the diffusion model approach make better use of the similarity bias than previous methods? This question goes unanswered.\n\nNot going beyond the PDBbind to identify alternative augmentation ligands (of which there are many, as structures aren't needed for inference) is a missed opportunity that weakens the paper as with the current evaluation framework many ligands can't be put into groups.  Replicating the ComBind evaluation would provide predefined groups of ligands while also making it possible directly compare to a conventional approach.\n\nThere are issues with using a time split to assess generalizability, but as this is the same split used with DiffDock it is appropriate to use for the comparisons performed here.\n\nThe overloading of \"k\" to mean two different things in Fig 2 is confusing.\n\nI found equations (4) and (5) confusing due to the overloading of z - the text says these two separate values (presumably equivalent to AF2 incoming and outgoing triangle attention edges) will be stacked, while the equations say they are summed.\n\n\"and since the C-C bond length is about 1.5A.\"\n\nTable 2 is apparently showing Top-10 results, but this fact is only stated in the main text. Why not replicate the reporting in Table 1?  \"Med.\" isn't defined, but is presumably median RMSD.\n\n4.4: \"Section\" is used when \"Figure\" is meant.  \"ifself\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)