FABind: Fast and Accurate Protein-Ligand Binding
Authors: Qizhi Pei
Kaiyuan Gao
Lijun Wu
Jinhua Zhu
Yingce Xia
Shufang Xie
Tao Qin
Kun He
Tie-Yan Liu
Rui Yan
Venue: NeurIPS 2023 poster (2023)
Keywords: protein-ligand docking

Abstract: Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.  FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at https://github.com/QizhiPei/FABind.

Bind: Fast and Accurate Protein-Ligand Binding
Qizhi Peil.5, Kaiyuan G Jinhuaa Zhu', Yingce Xia',
Shufang Xie Tao Qin', Kun He², Tie-Yan Liu', Rui Yan 167 Gaoling School Artificial Intelligence, Renmin University of China
School of Computer Science and Technology, Huazhong University of Science and Technology Microsoft Research A14Science
School of Information Science and Technology, University of Science and Technology of China
5 Engineering Research Center and Recommendation, Ministry of Education
Generation Intelligent Search
6 Beijing Key Laboratory of Big Data Management and Analysis Methods {qizhipei, shuf angxie, ruiyan) -ruc. edu. cn, {im_kai, brooklet60) Chust. edu. (lijuwu, yinxia, taoqin, tyliu] 8mi crosoft. t. com, teslazhu@mail ustc. edu. cn
Abstract
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is critical yet challenging task in drug discovery. Recent advancements deep learning have shown promise addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling- -based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression- based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, end-to-end model that combines pocket prediction and docking to achieve accurate informed and fast mootein-lignne binding. FABind incorporates unique ligand ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the pre- dicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code available at Githut
benchmark datasets,
Introduction
Biomolecular interactions are vital in the human body as they perform various functions within organisms 44 22. For example, protein-ligand binding (35), protein-protein interaction (17), protein- DNA interaction 18). and so on. Among these, drug-like small molecules (ligands) binding to protein is important and widely studied in interactions they facilitate drug discovery. Therefore, molecular docking, which involves predicting the conformation of a ligand when it binds to a target protein, serves crucial task, the resulting docked protein-ligand complex can provide valuable insights for drug development.
'Equal contribution. This work done during their internship Microsoft Research A14Science. Corresponding authors: Rui Yan (ruiyanQruc. edu. en) and Lijun Wu (1ijumm@microooff......oo com) httpa/////ggttbbbbbcc/c//hhhiiie
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Though important, fast and accurately predicting the docked ligand pose is super challenging. Two families of methods are commonly used for docking: sampling- and regression basond prediction. Most of the traditional methods lie in the sampling-based approaches as they rely on physics-infommed empirical energy functions to score and rank the enormous sampled conformations X
35
even with the use of deep bearning-based scoring functions for conformation evaluation [4 49]
these methods still need a large number of potential ligand poses for selection and optimization. DiffDock ? utilizes deep diffusion model that significantly improves accuracy. However, it still requires a large number of sampled/generated ligand poses for selection, resulting in high computational costs and slow docking speeds. The regression- based methods 19 32 III that use deep learning models to predict the docked ligand pose bypass the dependency on the sampling process. For instance. TankBind [3 proposes two-stage framework that simulates the docking process by predicting the protein-ligand distance matrix and then optimizing the pose. In contrast, EquiBind [4T] and E3Bind [51] directly predict the docked pose coordinates. Though efficient, the accuracy of these methods falls behind the sampling- baaedd methods. Additionally, the variation in protein sizes often requires the use of external modules to first select suitable binding pockets, which can impact efficiency. Many of these methods rely on external modules to detect favorable binding sites in proteins. For example, TankBind [31 and E3Bind [5]] take P2Rank (23 as priors for generating the pocket center candidates, which results in the need for a separate module (e.g..
affinity prediction in Tank and confidence module in E3Bind) for pocket selection, increasing the training and inference complexity.
To address these limitations, we propose FABind. Fast and Accurate docking framework in an end-to-end way. Bind unifies pocket prediction and docking, streamlining the process within single model architecture, which consists of series of equivariant layers with geometry-aware updates, allowing for either pocket prediction or docking with only different configurations. Notably, for pocket prediction, we utilize lightweight configurations maintain efficiency without sacrificing accuracy. In contrast to conventional pocket prediction, which only uses protein as input and forecasts multiple potential pockets, our method incorporates a specific ligand pinpoint the unique pocket that the ligand binds to. Integrating the ligand into pocket prediction is crucial as it aligns with the fundamental characterization of the docking problem. In this way. we achieve a quick (without the need for external modules such as P2Rank) and precise pocket prediction (see Section 5.1.
Several strategies are additionally proposed in FABind to make fast and accurate docking prediction. (1) Our pocket prediction module operates as the first layer in our model hierarchy and is jointly trained with the subsequent docking module to ensure seamless, end-to-end process for protein- ligand docking prediction. We also incorporate a pocket center constraint using Gumbel-Softmax. This way assigns probabilistic weighting to the inclusion of amino acids in the pocket, which helps to identify the most probable pocket center and improve the precision of the docking prediction. (2) We incorporate the predicted pocket into the docking module training using a scheduled sampling approach [4). This way ensures consistency between the training and inference stages with respect to pocket leverage, thereby avoiding any mismatch that may arise from using the native pocket during training and the predicted pocket during inference. In this way, FABind is trained on a variety of possible pockets, allowing to generalize well to new docking scenarios. (3) While directly
predicting the ligand pose [4]] and optimizing the coordinates based on the protein-ligand distance
map [31 are both widely adopted in the campling-based methods to ensure efficiency, we integrate both predictions in FABind to produce more accurate pose prediction.
To evaluate the performance of our method, we conducted experiments on the binding structure prediction benchmark and compared our work with multiple existing methods. Our results demon- strate that FABind outperforms existing methods, achieving a mean ligand RMSD of 6.4. This is a significant improvement over previous methods and demonstrates the effectiveness of our approach. Remarkably, our approach also demonstrates superior generalization ability, performing surprisingly well on unseen proteins. This suggests that our model can be applied to a wide range of docking scenarios and has the potential to be a useful tool in drug discovery. In addition to achieving superior performance, our method is also much more efficient during inference (e.g., 170x faster than Diff- Dock), making it fast and accurate binding framework. This efficiency is critical real-world drug discovery scenarios, where time and resources are often limited.
2

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Docking
FABind layer
teeative Sefinement
FABind layers
Interfacial Message Passing
OOOOOOOO
Attention Update
Pocket
Prediction
Gumbel Softnax
Independent Message Passing
COOO
FABind layers
M
XeRana Hege Ugand
Xe tettika
Bell
panxa
Representation
Receptor
Figure 1: An overview of FABind. Left: The pocket prediction module takes the whole protein and the ligand as input and predicts the coordinates the pocket center, where the ligand randomly placed at the center of the protein. After determining the pocket center, pocket is defined as set of amino acids within fixed radius around the center. Subsequently, the docking module moves the ligand to the pocket center and the ligand-pocket pair iteratively goes through the FABind layers to obtain the final pose prediction. M and N are the number of layers in pocket prediction and docking. Right: Architecture of FABind layers. Each layer contains three modules: independent message passing takes place within each component to update node embeddings and coordinates; cross-attentie captures correlations between residues and ligands and updates embeddings only; and interfacial message passing focuses on the interface, attentively updating coordinates and representations.
Related Work
Protein/Molecule Modeling, Learning effective protein and molecule representations is fundamental to biology and chemistry research. Deep learning models have made great progress in protein and molecule presentation learning. Molecules are usually represented by graph or string formats, and the graph neural networks (e.g., GAT [46), GCN (88) and sequence models (e.g.. LSTM [12].
Transformer (45)) are utilized to model the molecules. For proteins, the sequence models are the common choices since the FASTA sequence is the most widely adopted string format (38). Considering the 3D representations, incorporating the geometric informatiio//onntraini c.g., SE(3)- invariance or equivariance, is becoming promising to model the protein/molecule structures, and there are multiple equivariant/nvvrian graph neural networks proposed [9 20, 3 [6). Among them,
AlphaFold2 [19] is almost the most successful geometry- aware modeling that achieves revolutionary performance protein structure prediction. In our work, we also keep the equivariance when modeling the protein and ligand.
Pocket Prediction. Predicting binding pocket is critical in the early stages of tructtre-based drug discovery. Different approaches have been developed in the past years, including physical-chemical based, basee. and machine learning-
methods 44). Typical geometric- methods are SURFNET [26] and Fpocket [27]. and many alternatives have been proposed [47 6 42). Recently, machine learning-base
especially deep learning- based methods, have been promising, such as DeepSite [5] and DeepPocket [ which directly predict the pocket using deep networks, and some works utilize the deep scoring functions (e.g., affinity score) score and rank the pockets [36 50 Among them, P2Rank (23 is an sourcee tool that is widely adopted in existing works [1 to detect potential protein pockets.
this work, we distinguish ourselves from previous methods through the introduction of a novel equivariant module and the combination of two separate losses. Besides, we jointly train the pocket prediction module and the docking module, leveraging knowledge gained from the docking module to improve the performance of pocket prediction.
3

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Protein-Ligant Docking, Protein-ligand docking prediction is to predict the binding structure of the protein-ligand complex. Traditional methods usually take the physics-informed informed energy functions to score, rank, and refine the ligand structures, such as AutoDock Vina 43 SMINA [1], GLIDE [8. Recently, geometric deep learning has been attractive and greatly advances docking prediction. There are two distinct approaches in docking research. begression-baset methods, such as EquiBind A1], TankBind [31], and E3Bind [51], directly predict the docked ligand pose. On the other hand, sampling- based methods, like DiffDDock 77.. require extensive ligand pose sampling and optimization, but often yield more accurate predictions. Our work lies in the research of regression-basee methods with much less computational costs, but achieves comparable performance to sampling- -based approaches.
3 Method
An overview of our proposed FABind is presented in Fig. [ We first clarify the notations and problem definition in Section In Section [.2] we illustrate our FABind layer in detail. The specific design of the pocket prediction module is explained in Section 3.3 while the docking module is introduced
in Section 3.4 Furthermore, the training pipeline is comprehensively described in Section 3.5
3.1 Preliminaries
Notations. For each protein-ligand complex, we represent it as a graph and denote it as G li
(V = (V*, VD) : (E',E", (.))). Specifically, the ligand subgraph is G = (V* E`), where node (h, x,) E V is an atom, h; is the pre-extracted feature by TorchDrug [52]. and x; E R is the corresponding coordinate. The number of atoms is denoted as n'_ E is the edge set that represents chemical bonds in the ligand. The protein subgraph is G (VP EP), where node 1y (hj. xj) E VP is a residue, hj is initialized with the pre-trained ESM-2 (28) feature following DiflDock 7 and Xj E R is the coordinate of the C. atom in the residue. The number of residues is denoted as n?. Ed is the edge set constructed by a cut-off distance 8Å. The input of the pocket prediction module is G. We further denote the pocket subgraph g (V _P*), with n residues in the pocket. The pocket and ligand form a new complex as the input to the docking module: g'v* (V := (V', V V := {E E Elr²) }). where & defines edges in the external contact surface. Detailed edge construction rule is in Appendix Section A.4 For clarity, we always use indices i, k for ligand nodes, and j, K' for protein nodes.
Problem Definition. Given a bounded protein and an unbounded ligand as inputs, our goal is to predict the binding pose of the ligand, denoted as his"isi Following previous works, we focus on blind docking scenario in which we have zero knowledge about protein pocket.
3.2 FABind Layer
In this section, we provide comprehensive description of the FABind layer. For clarity, we use FABind in the pocket prediction module for demonstration.
Overview. Besides node-level information, we explicitly model pair embedding for each protein residue-ligand atom pair (i,)). We follow E3bind [51] to construct pair embedding Z,j via an outer product module (OPM): Zij = Linear ((Linear (haeearrh) X Linear(h;))) For the initial pair embedding, OPM is operated on the transformed initial protein/ligand node embedding h:/hy. As illustrated in Figure 1 each FABind layer conducts three-step message passing: (1) Independent message passing. The independent encoder first passes messages inside the protein and ligand to update node embeddings and coordinates. (2) Cross-attention update. This block operates to exchange information across every node and updates pair embeddings accordingly. (3) Interfacial message passing. This layer focuses on the contact surface and attentively updates coordinates and representations for such nodes. The fundamental concept behind this design is the recognition of distinct characteristics between internal interactions within the ligand or protein and external interactions between the ligand and protein in biological functions. After several layers of alternations, we perform another independent message passing for further adjustment before the output of pocket prediction/docking modules. Notably, the independent and interfacial message passing layers are E(3)-equivariant, while cross-attention update layer E(3)-invariant since it does not encode structure. These ensure each layer is B(3) -qu--vuuv....

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Independent Message Passing, We introduce variant of Equivariant Graph Convolutional Layer (EGCL) proposed by EGNN 39 as our independent message passing layer. For simplicity, here we only illustrate the detailed message passing of ligand nodes, while the protein updates are in a similar way. With the ligand atom embedding h and the corresponding coordinate x² in the 1-th layer, we perform the independent message passing as follows:
Oe (h/, big |x] -xul1)
mak
h1 - + Φh h/, E EvENGEE m
x x
[W(il')) EnENtiey (x) - x5) dz (max),
where de, 0z: On are Multi-Layer Perceptons (MLPs) [10] and (iil=") denotes the neighbors of node regarding the internal edges E` of the ligand.
Cross-Attention Update. After independent message passing, we enhance the node feature with cross-attention update over all protein/ligand nodes by passing the messages from all ligand/protein nodes. The pair embeddings are also updated accordingly. We also take the ligand embedding update for clarity. Given the ligand atom representations and the pair embeddings, we first perform multi-head cross-attention over all protein residues:
hh) = softmax, 1
( (M) (h) kg
hy h! Linear concatisnst
)
where q: (h) (K)
Vjj are linear projections of the node embedding, and 69 Linear (zl,) is linear transformation of pair embedding zly. The protein embeddings hJ, are updated similarly. Based on updated node embeddings hy and h/,, the pair embeddings are further updated by d/z =
(h)
z'g + OPM(h h)).
Interfacial Message Passing. With the updated protein and ligand representations, perform an interfacial message passing to update the included node features and the coordinates on the contact surface. Our interfacial message passing derives from MEAN [22] with an additional attention bias.
The detailed updates are as follows:
exp (q. Kij + bes)
Oij - EjEN(HETON exp (q. kij + b,y)'
h h1: Σ ESEN(HEE ViyVisi x++==x++ JEN((1gtn))
E ais (x- - xx) dev (vis),
where qi = do (h!), kij = du (1xx - x311 n1). Vij = do (1x² - , hn)). and bij = do (zlz), and og ox, Φv: Øb, der are MLPs. glps denotes the external edges between ligand and protein contact surface constructed by cut-off distance 10Å.
3.3 Pocket Prediction
In the pocket prediction module, given the protein-ligand complex graph G, our objective to determine the amino acids of the protein that belong to the pocket. Previous works such as TankBind and E3Bind both use P2Rank (23) to produce multiple pocket candidates. Subsequently, either affinity score (TankBind) or self-confidence score (E3Bind) is utilized to select the most appropriate docked pose. Though P2Rank is faster and better than previous tools, it is based on numerical algorithms and traditional machine learning classifiers. Furthermore, the incorporation of P2Rank necessitates the selection of candidate poses following multiple poses docking. These factors could potentially restrict the performance and efficiency of fully deep learning- based docking approaches.
In our work, we propose an alternative method by treating pocket prediction as binary classification task on the residues using the FABind layer, where each residue in the protein is classified as belonging to the pocket or not. Hence, the pocket prediction is more unified with the deep learning docking. Specifically, we use binary cross-entropy loss to train our pocket classifier:
1 n2
C
My log(p;) + (1 315) log(1 Pi)],
71p
5

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where np is the number of residues in the protein, and is the binary indicator for residue j (i.e.. 1 if belongs to a pocket, 0 otherwise). Py oMMLL(((ABind I)))r())),)) is the predicted probability of residue belonging to pocket, 9 is the protein-ligand complex graph, and is sigmoid function.
Besides the direct classification of each residue decide the pocket, the common practice of leveraging P2Rank pocket prediction is to first predict a pocket center coordinate, then a sphere near the pocket center under a radius 20Å. Therefore, we add a constraint about the pocket center to make more accurate prediction.
Constraint for Pocket Center. constrain the predicted pocket center, we introduce a pocket center regression task over the classified pocket residues. Given n²' predicted pocket residues V -f ug up ned) from our classifier, the pocket center coordinate is x" = nz -==1x;
Then we can add distance loss between the predicted pocket center x" and the native pocket center x This pocket center computation inherently involves discrete decisions selecting which amino acids contribute to the pocket. Hence, we apply Gumbel-Softmax [14] to produce a differentiable approximation of the discrete selection process. It provides probabilistic "hard" selection, which more accurately reflects the discrete decision to include or exclude an amino acid in the pocket.
exp((1og(p) gT)/Te)
122 k== (xpp(()g((H)) + g gw) )/Te)
where 9j is sampled from Gumbel distribution 9j = log( - log Um), Um Uniform(O, 1), and Te the controllable temperature. Then we use Huber loss [13] between the predicted pocket center
xP nF Σj= x" and the native pocket center x"* as the constraint loss,
C Huber (x", x
Training Loss. The pocket prediction loss is comprised of classification loss Cj and pocket center constraint loss cg²r, with a weight factor a 0.2,
Lpocket L.p + a.Lz
Pocket Decision. Our pocket classifier can output the classified residues in pocket. Following previous works [1], we do not directly take these predicted residues as the pocket. Instead, we calculate the center of these classified residues and take it as the predicted pocket center, and the predicted pocket in a sphere near the predicted center under a radius 20A. However, for some proteins, our classification model may predict each residue impossible to determine the pocket center. This likely due to an imbalance between the pocket and non-pocket residues. For these rare cases, we take the Gumbel-softmax predicted center x" as the pocket center to address this problem.
negative for a pocket, making it
3.4 Docking
In the docking task, given a pocket substructure GP*, our docking module predicts the coordinate of each atom in G'. The docking task is challenging since it requires the model to preserve E(3)- equivariance for every node while capturing pocket structure and chemical bonds of ligands.
adopted in docking FABind layers to refine the Iterative refinement. Iterative refinement [19 structures by feeding the predicted ligand pose back to the message passing layers several rounds. During refinement iterations, new graphs are generated and the edges are also constructed dynamically.
After l iterations (iterative refinement) of the N FABind layer alternations, we obtain the final coordinates x and node embeddings h and hf. Besides directly optimizing the coordinate loss, we additionally add distance map constraints to refine the ligand pose better. We reconstruct distance matrices in two ways. One is to directly compute based the predicted coordinates: Dij |x x/|l- The other is predict from the pair embeddings z by an MLP transition:
D. ML)(2 where each vector outputs a distance scalar.
Training Loss. The docking loss is comprised of coordinate loss Leoord and distance map loss Caist:
Ldocking Looord + Laistt

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Ligand RMSD
Centroid Distance
Pereentiles |
Below t
Percentiles
% Below
Average
Methods
25%
50%
75%
Mcan
2A
5
25%
50%
75%
Mean
2
SÅ
Runtime (s)
QVINA-W W
2.5
7.7
23.7
13.6
20.9
40.2
0.9
3.7
22.9
11.9
41.0
54.6
49
GNINA
2.8
8.7
22.1
13.3
21.2
37.1
1.0
4.5
21.2
11.5
36.0
52.0
146
SMINA
3.8
17.9
12.1
13.5
33.9
3.7
16.2
38.0
55.9
146*
GLIDE
2.6
16.2
21.8
33.6
26.9
14,4
36.1
48.7
1405
VINA
5.7
10.7
21.4
14.7
5.5
21.2
6.2
20.1
12.1
26.5
47.1
205*
EQUIBIND
3.8
6.2
10.3
8.2
5.5
39.1
2.6
7.4
40.0
67.5
0.03
TANKBIND
2.6
4.2
7.6
7.8
17.6
57.8
0.8
1.7
4.3
55.0
77.8
0.87
E3BIND
2.1
3.8
7.8
7.2
23.4
60.0
0.8
1.5
4.0
5.1
60.0
78.8
0.44
DIFDDCK (1)
2.4
4.9
8.9
8.3
20.4
51.0
0.7
1.8
4.5
5.8
54.1
76.8
2.72
DICEDOK (10)
1.6
3.8
7.9
7.4
32.4
59.7
0.6
1.4
3.6
5.2
60.7
79.8
20.81
DIFFDOCK (40)
1.5
3.5
7.4
7.4
36.0
61.7
1.2
3,3
62.9
80.2
82.83
FABIND
1.7
3.1
6.7
6.4
33.1
64.2
0.7
1.3
3.6
4.7
60.3
80.2
0.12
Table I: Flexible blind self-docking performance. The top half contains results from traditional docking software; the bottom half contains results from recent deep learning based docking methods. The last line shows the results of our FABind. The number of poses that DiffDock samples is specified in parentheses. We run the experiments of DiffDock three times with different random seeds and report the mean result for robust comparison. The symbol "*" means that the method operates exclusively on the CPU. The superior results are emphasized by bold formatting, while those of the second-best denoted by underline.
Leoord is computed as the Huber distance between the predicted coordinates and ground truth coordinates of the ligand atoms. Ldist is comprised of three terms, each of which is L2 loss between different components of the ground truth and the two reconstructed distance maps. Formally,
Edist
(Dy D.²)²
E E
(D:j Diy)² 2
+7 E. E (Di Det)
where Dij is ground truth distance matrix. In practice, we set f 1.0.
3.5 Pipeline
We first predict the protein pocket and then predict the docked ligand coordinates a hicrarchical unified framework. However, the common approach is only use the native pocket for docking in the training phase, which is known as teacher-forcing 24 training. Therefore, there is a mismatch in that the training phase takes the native pocket while the inference phase can only take the predicted pocket since we do not know the native pocket in inference. To reduce this gap, we incorporate a scheduled training strategy to gradually involve the predicted pocket in the training stage instead of using the native pocket only. Specifically, our training pipeline consists of two stages, (1) in the initial stage, since the performance of pocket prediction is poor, we only perform the docking training: (2) In the second stage, with the improved pocket prediction ability, we then involve the predicted pocket into docking, where the native pocket is still kept in docking. The ratio between the predicted pocket and the native pocket is 1: 3.
the native pocket to
Comprehensive Training Loss. Our comprehensive training loss comprises two components: the pocket prediction loss and the docking loss,
Cpocket + Ldocking
(I)
4 Experiments
4.1 Setting
Dataset. We conduct experiments on the PDBbind v2020 dataset [29], which is from Protein Data Bank (PDB) [5] and contains 19,443 protein-ligand complex structures. To maintain consistency with prior works 41 31) we follow similar preprocessing steps (see Appendix Section A. for more details). After the filtration, we used 17, 299 complexes that were recorded before 2019 for training purposes, and an additional 968 complexes from the same period for validation. For our testing phase, utilized 363 complexes recorded after 2019.
7

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Ligand RMSD
Centroid Distance
Percentiles
% Below f
Percentiles J
% Below
Average
Methods
25%
50%
75%
Mcan
2
5A
25%
50%
75%
Mean
2
SÁ
Runtime
QVINA-
28.1
31.9
6.5
15.2
GNINA
4.5
13.4
27.8
16.7
13.9
27.8
2.0
10.1
27.0
15.1
25.7
39.5
146
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
146
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
1405
VINA
7.9
27.1
18.7
12.0
2.4
15.7
26.2
16.1
20.4
37.3
EQUIBIND
5.9
14.3
11.3
0.7
18.8
2.6
6.3
12.9
16.7
43.8
0.03
TANKBIND
3,4
5.7
10.8
10.5
3.5
43.7
1.2
2.6
8.4
8.2
40,9
70.8
0.87
E3BIND
3.0
6.1
10.2
10.1
6.3
38,9
1.2
2.3
7.0
7.6
43.8
66.0
0.44
DIFFDOCK (1)
4.1
7.2
18.2
12.5
8.1
33.1
1.4
3.7
16.7
10.0
33.6
58.3
2.72
DIFKDOCK (10)
3.2
16.5
11.8
14.2
38.7
1.1
2.8
39.7
62.6
20,81
DurDOCK (40)
2.8
16.3
12.0
42.3
1.0
2.7
14.2
43.3
62.6
82.83
FABIND
2.2
8.3
7.7
19.4
60.4
0.9
1.5
57.6
75.7
0.12
Table 2: Flexible blind self-docking performance on unseen receptors.
Model Configuration. For pocket prediction, use M FABind layer with 128 hidden dimensions. The pocket radius is set to 20A. As for docking, we use N = 4 FABind layers with 512 hidden dimensions. Each layer comprises three-step message passing modules (refer Fig. D We perform total of k = 8 iterations for structure refinement during the docking process. Additional training details can be found in the Appendix Section A.2
Evaluation. Following EquiBind [4], the evaluation metrics are (I) Ligand RMSD, which calculates the root-mean-square deviation between predicted and true ligand atomic Cartesian coordinates, indicating the model capability of finding the right ligand conformation at atom level; (2) Centroid Distance, which calculates the Euclidean distance between predicted and true averaged ligand coordinates, reflecting the model capacity to identify the right binding site. To generate an initial ligand conformation, we employed the ETKDG algorithm (37 using RDKit (25), which randomly produces low-energy ligand conformation.
4.2 Performance in Flexible Self-docking
In the context of flexible blind self self-docking, where the bound ligand conformation is unknown and the model is tasked with predicting both the bound ligand conformation and its translation and orientation, we observe the notable performance of our FABind. Across most metrics, Bind ranking either Our FABind exceeds the DiffDock with 10
the best second best, presented in Table [ sampled poses. Although FABind may not achieve the best performance in terms of the <2Á metric, it demonstrates exceptional proficiency with mean RMSD score of 6.4À. The comparatively lower performance in the <2Å metric can be attributed to the optimization objective of DiffDock, which primarily focuses on optimizing ligand poses towards achieving <2Á accuracy. FABind also gets comparative performance on Centroid Distance metries. Overall, the experimental results showcase the remarkable efficacy of FABind.
4.3 Performance in Self-docking for Unseen Protein
To perform a more rigorous evaluation, we apply an additional filtering step to exclude samples whose UniProt IDs of the proteins not contained in the data that is during training and validation. The resulting subset, consisting of 144 complexes, was then used to evaluate the performance of FABind. The results of this evaluation presented in Table demonstrating that FABind surpasses other deep learning and traditional methods by a significant margin across all evaluation metrics. These findings strongly indicate the robust generalization capability of FABind.
4.4 Inference Efficiency
We conducted an inference study to demonstrate the efficiency of FABind. The statistics regarding the average time cost per sample are presented in Table Comparing our method others, we observe that the inference time cost of FABind ranks second lowest. Notably, when compared to DiffDock with sampled poses, our FABind achieves better docking performance while being over 170 times faster. The efficiency of our method can be attributed to eliminating the need for an external

---

pocket selection module, and the sampling and scoring steps used by other methods. By doing so, our FABind produces significantly faster docking. While EquiBind exhibits superior inference speed. it performs poorly in the docking task. These compelling findings strongly support the claim that our FA Bind not only highly fast but also accurate.
Further Analysis
5.1 Pocket Prediction Analysis
In this study, We conduct an independent analysis of our pocket prediction module. To evaluate its performance, we compared with two existing methods, TankBind and E3Bind, both of which use P2Rank to first seg- ment the protein multiple pocket blocks, and then apply confidence module for ranking and selection. The selected pockets are used to compare with our pre- dicted pockets. The pocket prediction performance is
DCC Below
Methods
3λ
4Á
5A
TANKBIND
18.2
32.0
39.9
E3BIND
26.7
35.8
50.1
P2RANK
36.4
50.1
57.0
FABIND
42.7
56.5
62.8
LIGAND INFORMATION
36.9
51.5
59.0
measured by the distance between the predicted pocket center and the center of the native binding site (DCC) metric, which is a widely-used metric pocket pre-
CENTER CONSTRAINT
8.8
22.9
31.7
Table 3: Results of pocket prediction.
diction task []. We use thresholds of 3', 4A, and 5A, to determine successful predictions. From [ we can see that: (1) removing the center constraint (classification only) significantly hurts the prediction performance. (2) The integration of ligand information into the pocket prediction task also enhances performance and efficiency. Previous two-stage methods involve initial docking for multiple pocket candidates, followed by the selection of the optimal candidate. Nevertheless, the majority of potential binding sites are irrelevant to the particular ligand [5T]. Our FABind further improves the inference efficiency as our pocket prediction is essentially designed for predicting the most probable pocket for the specific ligand. (3) Combined with center constraint, even without incorporating ligand information, our pocket prediction performance outperforms TankBind and E3Bind, which shows rational design of the pocket prediction module is effective.
5.2 Ablation Study
In this subsection, we perform several ablation studies to analyze the contribution of different components, the results for selected metrics are reported in Table 4 We examine four specific settings: removal of the scheduled training strat- egy (teacher forcing); removal of the distance map related losses; removal of the iterative re- finement; and removal of the cross-attention up- date. From the table, we can see that: (I) each
RMSD Mean (A),
RMSD Below 2
Methods
FABIND
6.4
33.1
SCHEDULED COORD LOSS ONLY
28.7
6.9
16.3
REFINEMENT
22.5
NO CROSS-AT TENTION
6.4
21.4
Table 4: Results of ablation study.
of the components contributes to the good performance of our FABind. (2) The combination of distance map losses impacts most of the docking performance. (3) The remaining components
primarily contribute to improvements fine-grained scales (<2A), A full ablation study can be found in Appendix Section C
5.3 Case Study
FABind can identify the right binding pocket for a new large protein. In Fig. a). the large protein target (PDB 6NPI) is not seen during training, thus posing significant challenge to accurately locate the binding pocket. Though predicted poses other deep learning models (DiffDock, E3Bind. TankBind, and EquiBind) are all off-site, FABind can successfully identify the native binding site and predict the binding pose (in green) with low RMSD (3.9A), showing the strong generalization ability,
FABind generates ligand pose with better performance and validity. Fig. ( shows the docked results of another protein (PDB 6G3C). All methods find the right pocket, but our FABind aligns best RMSD (0.8Å) with the ground truth ligand pose. In comparison, E3Bind produces a knotted pose where rings are knotted together which not valid. DiftDock also produces the same accurate ligand

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RMSD from
ground
FABinds 3.9
DinDodia 13.9
m38
TankiBind:
23.2
EquiBind:
20.0
RMSD from
ground
Dirtbodc 0.5
E38
TankBiind:
(4) PDB: GNPI
EquiBind:
5.8
(b) 6G3C
Figure 2: Case studies. Pose prediction by FABind (green), DiffDDck (wheat), E3Bind (magenta), TankBind (cyan), and EquiBind (orange) are placed together with protein target, and RMSDs to ground truth (red) are reported. (a) For large unseen protein (PDB 6NPI), FABind successfully identifies the pocket, while the others all off-site. (b) For the other protein (PDB 6G3C), all models find the right pocket, among which FABind predicts the most precise and valid binding pose as the Difk but with faster speed.
pose but is much slower. These show that FABind can not only find good pose with high speed but also maintain structural rationality.
6
Conclusion
In this paper, we propose an end-to-end framework, FABind, with several rational strategies. We propose pocket prediction mechanism to be jointly trained with the docking module and bridge the gap between pocket training and inference with scheduled training strategy. We also introduce novel equivariant layer for both pocket prediction and docking. For ligand pose prediction, we incorporate both direct coordinate optimization and the protein- distance map-based refinement. Empirical experiments show that FABind outperforms most existing methods with higher efficiency. In future work, we aim to better align the pocket prediction and docking modules and develop more efficient and effective tools.
Acknowledgments
We would like thank the reviewers for their insightful comments. This work was partially supported by National Natural Science Foundation of China (NSFC Grant No. 62122089), Beijing Outstanding and Intelligent Social Governance
Young Scientist Program NO. BJIWZYTH0120111000200 Platform. Major Innovation & Planning Interdisciplinary Platform for the "Double-First Class" Initiative, Renmin University of China.
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A More Detailed Descriptions
A.1 Dataset Preprocessing
As we stated in paper Section 4.1, PDBBind v2020 dataset (29 contains 19,443 ligand-protein complex structures, and we pre-process the structures as follows. First, we only keep complex structures whose ligand structure file (in sdf or mo12 format) can be processed by RDKit [25] or TorchDrug (52), leaving 19,126 complexes. Then, to address the multiple equally valid binding ligand pose issue for symmetric receptor structures, we only keep the protein chains that have an atom within 10 Á radius of any atom of the ligand. We further filter out complexes in which the contact (distance is within 10A) number between ligand atom and protein amino acid C. are less than or equal to 5, or the number of ligand atom is more than or equal to 100. After applying these filters, 18, 630 complexes are left. Finally, we process the remaining complexes with the time split as described in EquiBind [41.
A.2 Experiment Settings
Baseline. We compare our model with traditional score-based docking methods and recent geometry- based deep learning methods. For traditional docking methods, QVina-W, GNINA [33], SMINA [21. GLIDE 8 and AutoDock Vina 433 are included. For deep learning methods, EquiBind 41].
TankBind 31, E3Bind [5T] and DiftDock 7 are included.
We corrected results for the deep learning baselines including EquiBind, TankBind, and E3Bind. The corrected results adopt post-optimization methods on model outputs, including fast point cloud fitting (used in EquiBind) and gradient descent (used in TankBind and E3Bind), which can further enforce geometry constraints within the ligand. For TankBind, the post-optimization method used to get final ligand coordinates through the predicted distance matrix, which is essential for distance to coordinate transformation. However, for a fair comparison, the reported average runtime of EquiBind, TankBind, E3Bind, and FABind is the uncorrected version without post-optimization. The reported baseline results are mainly derived from the original paper of E3Bind [51]. For the DiffDock results, as the results of the sample-based method are unstable, we reproduce the results using its pre-trained checkpoin(" ! Specifically, we run the DiffDock inference codes with three random seeds and report the average results.
Training and Evaluation. The training process consists of two stages, In the initial warm-up stage, only the native pockets are used for docking. Once the pocket prediction performance reaches a certain threshold (specifically, when the center coordinate distance between the predicted center and ground truth less than 4A), the training progresses to the second stage. During the second stage, the predicted pockets are integrated into the docking training process. The sampling protocol involves a 75% probability of selecting the ground truth pocket and a 25% probability of selecting the predicted pocket. Note that the task of pocket prediction is consistently incorporated into the entire training process. Following E3Bind [51, we also apply normalization (divided by 5) and unnormalization (multiplied by 5) techniques on the coordinate and distance. Additionally, to improve
the generalization ability of the model, the pocket is randomly shifted from -5 to 5 in all three axes during training. FABind models are trained for approximately 500 epochs using the AdamW [30] optimizer on 8 NVIDIA V100 16GB GPU with batch size set to 3 on each GPU. The learning rate 5e 5, which is scheduled to warm up the first 15 epochs and then decay linearly. To further enforce geometric constraints, we also incorporate local atomic structures (LAS) constraints in the training process by ensuring the distances between ligand atoms and i in the transformed conformer (X) by the model are consistent with those in the initial low-energy conformer (Z) for atoms that are either 2-hop neighbors or in the same ring structure, as proposed in EquiBind 41].
A.3 Ligand and Protein Feature Encoding
As stated in paper Section 3.1, construct ligand feature by TorchDrug 522 toolkit and protein feature with the pre-trained ESM-2 model. Here we give a detailed description of the encoding. For ligand compound, the node embedding h, is a 56-dimensional vector containing the following features: atom number; degree; the number of connected hydrogens; total valence; formal charge;
14

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Table 5: PDBBind blind docking on apo proteins. The top half contains results from traditional docking software; the bottom half contains results from recent deep learning based docking methods. The last line shows the results of our FABind. No method received further training on ESMFold generated structures.
Apo ESMFold proteins
Top-1
RMSD
Method
%<2
Med.
GNINA
2.0
22.3
SMINA
3.4
15.4
EQUIBIND
1.7
7.1
TANKBIND
10.4
5.4
SMRAA
10.0
PNRANK+GNINA
8.6
11.2
EQUIBIND+SMINA
4.3
8.3
EQUIBIND+GNINA
10.2
8.8
DIFFDOCK (10)
21.7
5.0
DIFFDOCK (40)
20.3
FABIND
24.9
4.2
whether or not it is in an aromatic ring. For protein target, we directly use the pre-trained 33-layer ESM-2 (28) mode which contains 650M parameters and is trained UniRef 50M dataset. The node feature the protein graph is derived from the amino acid feature and the hidden size is 1280.
A.4 Model Architecture Details
Edge Construction. We now introduce how to construct the edges our FABind layers. For clarity, use FABind in the pocket prediction module for demonstration. The cut-offs are set to the same in both pocket prediction and docking. As defined in the paper, we have three types of edges, E ; -E", ED, E.r). for ligand, protein, and ligand-protein interface, respectively. E' and Ee are constructed from independent context, while Edp is constructed from external interface, For the independent context a ligand, we directly refer to chemical bonds as constructed edges El with biological insight that a molecule keeps its chemical bonds during the process. For the independent context of protein, Ed is defined as the edges connecting to nodes when the spatial distance is below cutoff distance Cin- We set Cn 8.0 in work following Kong et al. (22). Note that independent edges for ligands and proteins differ. For external edges go,, we also add edges with a spatial radius threshold Cex 10.0 following TankBind [31.
Global Nodes. MEAN 22 demonstrates that the global nodes intensify information exchange during message passing. Therefore, we insert global node into each component (ligand or protein) of the complex as well. global node connects all nodes in the same component and the other global node. The coordinates are initialized as zero tensors and can be updated during feed-forward.
Iterative Refinement. Iterative structure refinement has been proved critical design in structure prediction task [19]. It allows the network to go deeper without adding much computational overhead. Specifically, we update coordinates during all iterations and update hidden representations only the last iteration. To stabilize the training process and save memories, we stop the gradients except for the last iteration. In our implementation, we also accelerate training speed by randomly sampling an iteration number less than or equal to the configuration N for each batch, while always refining N iterations during inference.
'The pre-trained ESM-2 checkpoint can be found at /tttppp//a...oappplleeeeeeeeceaa models/ lemellssesmmm_mmmmme _eee2__333_____0 _33_650M___0 UR5OD
15

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Ligand RMSD
Centroid Distance
Percentiles
% Below f
Percentiles +
% Below
Methods
25%
50%
75%
Mcan
2
5
25%
50%
75%
Mcan
2
5
FABIND
1.7
6.4
33.1
64.2
60.3
NO SCHEDULED SAMPLING
1.7
3.5
7.0
6.4
28.7
63.4
0.7
1.5
3.5
4.5
60.3
82.6
COORD LOSS ONLY DIST. (1)
2.6
4.1
6.9
60.9
1.0
4.5
53.9
1.9
3.5
7.4
6.5
26.2
63.4
4.1
56.7
80.0
CROSS-ATTENTION
2.2
4.2
7.0
6.4
21.4
59.9
0.9
1.9
4.1
4.5
52.3
80.4
NO INDEPENDENT MP
2.1
3.8
7.0
6.6
22.6
61.7
0.8
1.5
3.3
4.6
59.8
82.6
NO ITERATIVE
2.2
4.1
7.2
6.6
22.5
58.5
0.9
1.9
4.7
4.7
52.3
80.4
NO ESM-2
1.9
3.6
6.3
27.5
61.2
3.8
56.2
WITH POST OPTIMIZATION
1.8
3.5
6.6
30.9
61.4
59.8
Table 6: Results of full ablation study.
B Apo-Structure Docking
As stated in DiffDock [7]. the PDBBind benchmark primarily focuses on evaluating the ability of docking methods to dock ligand its corresponding receptor holo-structure, which is simplified and less realistic scenario. However, in real-world applications, docking is often performed on apo or holo-structures that are bound different ligands. To address this limitation, DiffDock proposed new benchmark that combines the crystal complex structures of PDBBind with protein structures predicted by ESMFold (28. In order validate the efficacy of our FABind in the apo-structure docking scenario, we also evaluated its performance under the same settings with DiffDock. In order to validate the efficacy of our FABind in the apo-structure docking scenario, we implement the same experimental setup as DiffDock. For 363 test samples, we first extract their sequences from PDBBind and use esmf old_vidvvvoo v1 to predict their structures, from which process we assume that the apo protein structures are obtained. One sample (PDB: 6OTT) is filtered out due to aut-of-memory error. Then we align the rest the samples with PDBBind, where 12 samples are further excluded due to memory limitations, the same as DiffDock. The results in Table demonstrate that FA Bind outperforms DiffDock, achieving an RMSD of less than 2 24.9% of the complexes generated by ESMFold. This demonstrates the strong predictive capacity of FABind for apo-structure predictions.
C
Study
C.1
Full Ablation
The comprehensive ablations are listed in Table 6 We can observe that each of the components contributes to the good performance of our FABind. Firstly, the scheduled training strategy, when removed, leads to a slight decrease in performance for challenging cases (e.g., RMSD 75%).. This indicates that the scheduled training strategy contributes positively to handling difficult scenarios. Regarding loss construction, the inclusion of the distance map loss is crucial, and solely utilizing the first term of distance map loss (i.e., the distance loss between Dij and Dij in Ldists which we call "coord loss dist. loss (1)" T does not yield optimal results. The architecture design also has substantial impact. The removal of the cross-attention update and independent message passing severely impair the model's s ability handle favorable cases (e.g., RMSD 25%). This suggests that cross-attentiog update and independent message passing are both vital for capturing important structural dependencies. Furthermore, iterative refinement found to be indispensable for most structure prediction models, including ours. In terms of feature representation, the utilization of ESM-2 features for residues proves to be most beneficial for challenging cases. enhances the model's capability to handle difficult scenarios effectivelyy. Lastly, post-optimization does not significantly affect the overall performance but ensures that the generated ligand conformations are chemically more rational.
C.2 Iterative Refinement
In this section, we investigate the impact of iterative refinement on our model. We utilize our best-performing model and evaluate its performance using different iterations during inference. The
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Ligand RMSD
Centroid Distance
Percentiles
% Below
Percentiles !
% Below f
Average
Iteration
25%
50%
75%
Mcan
2
5
25%
50%
75%
Mean
2
5
Runtime
ITERATION=1
3.1
4.8
7.9
7.3
51.8
1.2
2.0
5.3
49.9
74.1
ITERATION=2
2.4
3.8
7.7
6.8
20.1
60.1
0.9
1.5
4,9
56.3
77.1
0.05
IEERATION=4
1.8
3.3
7.2
6.5
28.4
63.6
0.7
1,4
3.8
4.8
59.5
78.8
0.07
ITERATION=6
1.8
3.2
7.0
6.4
30.9
64.5
0.7
1.3
3.7
4.7
60.6
79.6
0.10
ITERATION=8
1.7
3.1
6.7
6,4
33.1
64.2
0.7
1.3
3.6
4.7
60.3
80.2
0.12
ITERATION=
3.1
6,6
6.4
32.5
65.3
0.7
1.3
4.7
60.6
80.4
0.16
ITERATION=12
3.1
6.6
32.5
65.6
0.7
14
4.7
61.7
80.4
Table 7: Inference on different iterations.
ground truth (A)
FABind: 2.7
DiliDddi
E38ind
TankBinndt 74
EquiBind:
RMSD from
ground (A)
FABind: 1.9
DifDDdic 2.2
TankBBind: 3.4
(a) PDB: 6N93
EquiBind: 5.7
Pb) 6JB4
Figure 3: Additional case studies. Pose prediction by FABind (green), DiffDock (wheat), E3Bind (magenta), TankBind (cyan), and EquiBind (orange) placed together with protein target structure, and RMSD to ground truth (red) are reported. (a) For unseen protein (PDB 6N93), FABind, E3Bind, and TankBind successfully identify the pocket, among which FABind predicts the most precise binding pose with the lowest RMSD 2.7Á, while the other methods are all off-site. (b) For PDB 6JB4, all deep learning models find the right pocket, among which FABind predicts the most precise binding pose with the lowest RMSD 1.9A.
results are reported in Table From the table, we can find that (denote the number of iterations as i, 1 < i < 12): (1) the performance improves as i increases. (2) The results tend to be stable when i increases to some extent, (3) When 8, the results are generally optimal. However, the results are similar when 4 < i < 12. Thus, smaller value of can be used for better efficiency.
C.3
More Cases
Here show cases test further verify the ability of FABind in finding the correct pocket for unseen protein and docking at the atom level. From Fig. 3]a in PDB 6N93, the protein unseen in the training set, and only the predictions of FABind, E3Bind and TankBind are in the right pocket, among which FABind predict the most accurate binding pose (RMSD 2. 2.7A). From Fig. B)), PDB 6JB4, though every method correctly finds the native pocket, FABind predicts the most accurate ligand conformation (RMSD 1.9Å).
D Broader Impacts and Limitations
Broader Impacts. Developing and maintaining the computational resources necessary to conduct Al-based molecular docking requires considerable resources, which may lead to a waste of resources.
Limitations. In FABind, we represent the protein structure at the residue level, assuming the rigidity of the protein. While many existing molecular docking methods adopt a similar protein modeling strategy, we believe that employing atom-level protein modeling and incorporating protein flexibility into modeling process could yield improved results. FABind not optimally designed to accommodate scenarios with multiple binding sites, wherein a ligand has many potential binding
17

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sites on protein. Additionally, it is not well-suited for situations where a ligand can adopt multiple binding conformations within a specific pocket of protein. Generative modeling 7 (sample-based method) presents a reasonable approach to address these two scenarios. However, due to the scope and limitations of our current work, we have decided defer these aspects to future research.
18


Reviews:
[{"review_id": "U5ZevGJy6p", "reviewer": "Reviewer_wgwB", "summary": "**Overview**. The paper proposes a model for flexible blind docking, where, without knowledge of the protein pocket, the model must predict the configuration (translation, rotation, conformation) of a given ligand bound to a given protein. The authors categorise prior deep-learning methods as either regression- or sampling-based, with, e.g., TankBind, EquiBind, and E3Bind being regression-based and, e.g., DiffDock being sampling-based. The authors describe a trade-off: while sampling-based methods are accurate, they are slow in inference time; and while regression-based methods are fast in inference time, they are inaccurate. The authors propose a fast regression-based method that they claim rivals the accuracy of sampling-based methods. The method breaks the task of blind docking into two subtasks, **pocket prediction** and **docking**, and uses a learned equivariant **representation** of proteins and ligands to perform them. The model is reported to have comparable performance to DiffDock (SOTA) in blind docking, and overall better performance than DiffDock when unseen proteins are used.\n\n**Representation**. Their model represents proteins as amino acid residue graphs with $C_\\alpha$ coordinates, edges between $8 Å$-away residues, and node features generated by ESM-2. Ligands are represented as atom graphs with edges for every bond a node features generated by TorchDrug. Pair embeddings between protein nodes and ligand nodes are computed with an outer product of the protein features and ligand features (following E3Bind). The authors propose a layer, called FABind, to update the coordinates and features of the protein and ligand graphs, which consists of three components. The first component, Independent message passing, updates the protein and ligand graphs' coordinates and features by performing Equivariant Graph Convolution (from EGNN) within the graphs separately. The second component, Cross Attention Update, updates the protein and ligand graphs' features (as well as the pair embeddings) by applying attention with message passing over all residue-atom pairs. The third and final component, Interfacial Message Passing, updates the coordinates and features of nodes in the protein and ligand graphs that are on a contact surface, using attention and message passing on the contact surface.\n\n**Pocket prediction and Docking**. Using the updated protein and ligand graphs, binary classification is done on the protein graph to determine which residues belong to the binding pocket using a binary cross-entropy loss. Then, using Gumbel-Softmax, a weighted mean of the decided pocket residues is computed as the predicted centroid of the pocket, and finally, the Huber loss between the predicted centroid and the true centroid is computed as a loss. The ligand coordinates and features are fed through the FABind layer several times (\"iterative refinement\"). One loss is computed by the Huber distance between predicted and true coordinates. Another loss is computed by the sum of three $L_2$ sub-losses. The first sub-loss is between the true distance matrix (a matrix of distances between pairs of atoms in the ligand) and the distance matrix computed from FABind's predicted ligand coordinates. The second sub-loss is between the true distance matrix and a distance matrix computed with an MLP from FABind's predicted pair embeddings. The third sub-loss is between these two predicted distance matrices. Training is done in a scheduled manner (following so-called teacher-forcing), where the model is first trained for docking with the known pocket; and then, once the pocket prediction is reasonably good, it is used for docking.\n\n**Results**. The model is evaluated on PDBBind 2020, with similar preprocessing to EquiBind, TankBind, and DiffDock. The model is reported to outperform all other models in the percent of predictions with ligand RMSD below 5Å, and all models except DiffDock with ligand RMSD below 2Å. The model is also reported to outperform DiffDock when presented apo structures of the proteins.", "questions": "1. How does the model perform when independent message passing is removed? \n2. How are the external contact surface edges defined? I did not see an explanation.\n3. How does the model perform when used together with a searched-based method (i.e. \"traditional docking software\")?\n4. In the apo structure docking experiments, where is most of the flexibility in the receptors (i.e. mostly in the backbone or mostly in the side chains)? Could you show how the model performs when presented with receptors that have most of their flexibility in the backbone (and when most of the flexibility is in side chains)?\n5. How does the model handle the existence of multiple possible binding sites and/or multiple possible bound ligand configurations? Could you please show the models in these cases (and compare with DiffDock)?\n6. Could you please elaborate on which pre-trained features were used in the protein and ligand graphs?\n7. Why were edge features not included (e.g. single and double bonds in ligands)?", "limitations": "1. The model treats proteins as rigid, which is not true for a large portion of important proteins (e.g. GPCRs), though a reasonable assumption for many.\n2. The model assumes a unique binding site and ligand configuration (there could be multiple possible sites and configurations).", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The model is significantly faster than DiffDock (SOTA), while still achieving comparable performance in blind docking.\n2. The model is reported to perform better than DiffDock in blind docking with unseen proteins.\n3. The model does not require additional geometric constraints as in DiffDock (i.e. group transformation manifolds), making the formulation a bit easier to understand at times.", "weaknesses": "1. The results are not significantly better than prior methods: the percent of predictions with a ligand RMSD below 5Å is higher than that of other models. However, the percent of predictions with a ligand RMSD below 2Å is not greater than that of DiffDock. Any ligand prediction with RMSD above 2Å is not very useful in itself (but may be useful for \"warm-starting\" and speeding up search-based methods).\n2. The model seems to assume that there is one unique binding pocket that a given ligand will bind to. In reality, this is not true, as, in addition to an orthosteric binding site (the natural one), there might exist other allosteric binding sites (not the natural one). Therefore, sampling-based methods seem to have the upper hand in this case, as they are able to capture multiple different binding modes.\n3. It seems that the model also assumes that there is one unique bound ligand configuration (since this is a regression-based model) which might not be true either.\n4. The structure of the paper was a bit difficult follow and the related work section seemed a bit short. I would appreciate a more detailed comparison of this model and other prior models. A detailed explanation of how this model is different from previous models should be included.\n5. I could not get the code to run because I could not find the module `metrics_to_tsb`. Please advise.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your insightful comments. We appreciate your time and effort and would like to address the concerns.\n\n> **W1: Comparison with DiffDock**\n\nThanks for careful comparison. DiffDock is a sample-based method, and each sampling will generate a sampled pose and it employs an extra confidence model to select the most appropriate conformation. The large sampled candidates contributed to its performance.\nWe acknowledge the case on <2Å with 40 sampled poses, but would like to point out that FABind achieves comparable/better accuracy at a sample size of 10 and is several orders of magnitude faster(170 times faster). When compared to DiffDock with 40 sampled poses, FABind is slightly worse but over 690 times faster. This efficiency is particularly vital in large-scale virtual screening. Another point is that the confidence model in DiffDock is specifically trained in terms of <2Å metric, this contributes to its good performance when using 40 sampled poses. \n\nIn addition, FABind shows better performance than DiffDock(40) in blind docking with unseen receptors(Tab.2) and apo-structure docking(Appendix Tab.1). \n\nMoreover, FABind is agnostic to training algorithm, enabling easy integration with diffusion. From the results in Tab.1, we believe even better results could be achieved when adapted to a sample-based method.\n\n> **W2&W3&Q5: Multiple binding sites/ligand configurations, cases compared with DiffDock**\n\nThanks for pointing this concern. We agree there maybe multiple binding sites/pockets. However, our method is mainly designed to predict pocket and ligand pose with the maximal probability(strongest binding strength). This also aligns with data perspective, as PDBBind dataset focuses on the strongest natural binding pose to the site. Besides, we give some discussions about these special cases with multiple binding sites and multiple conformations. \n\n(1) **Multiple binding sites.** The most important case is that such proteins are symmetric or multimeric proteins. For these cases, the ligand can equally bind to any of these symmetric pockets as correct one. We can simply take any of symmetric part of the protein to search correct pocket. In current process, we solve majority of this issue by only keeping protein chains which have an atom within a 10Å radius of any ligand atom. Such cases and the comparison with DiffDock can be found in Figure 2 of the uploaded PDF, in which both our method and DiffDock can find the correct pocket. In addition, we can also easily use external tools to recognize symmetric proteins and post-process the results in case the regression method has this limitation. \n\n(2) **Multiple bound conformations.** Regression methods truly suffer from the mean prediction if there are multiple bound conformations. It’s true that sample methods may generate multiple poses, but the validation is also not easy. And we usually care about the one with lowest energy, which aligns with current training data and is what we focus on. \n\n> **W4: Paper structure and related works**\n\nWe apologize for any confusion. The logic in the method is to first introduce FABind layer, which is used in the whole framework that contains pocket prediction and docking prediction we introduced next. Finally we summarize with a pipeline(training method) subsection. For related works, due to the space limitation, we’ll give more comparison in later revision.\n\n> **W5: `metrics_to_tsb`**\n\nSorry for oversight. This module only stores metrics for TensorBoard and can be removed. We have updated the code to AC according to the rebuttal rule. You can contact AC if needed.\n\n> **Q1: Remove Independent Message Passing(IMP)**\n\nIMP is crucial as it represents internal force field updates within ligand or protein. Removing it would lead to an inability to update node coordinates that are distant from the contact surface. We have conducted an ablation in the Tab.3 of the uploaded PDF to prove this.\n\n> **Q2: External contact surface edges**\n\nDue to space constraints, we have placed the definition in Appendix Sec 1.4. For the edges at ligand-protein interface, we employed a cut-off distance less than 10Å to determine whether an edge should be constructed between two points. \n\n> **Q3: Together with traditional docking software**\n\nThanks for the advice. We conduct the following study with SMINA and our FABind. We use FABind to predict the pocket first, and then feed into SMINA for docking. Results are in Tab.1 of the uploaded PDF. When without specifying our FABind predicted pocket(docking on entire protein), SMINA performs less accurate. In contrast, with our predicted pocket, the accuracy is improved, but still worse than FABind. This proves that our FABind can predict effective pockets and the docking module is superior than SMINA. \n\n> **Q4: Apo-structure docking**\n\nThere might be some misunderstandings about the flexibility in this setting. The apo-structure docking, proposed by DiffDock, assumes protein is still rigid, meaning there is no flexibility in backbone and side chains. Compared to the holo-structure(bound), which is provided by PDBBind, apo-structure(unbound) is computationally generated(by ESMFold) and aligned with original holo-structure. We follow the same setting as DiffDock and will clarify this point in the revised version.\n\n> **Q6: Pre-trained feature usage**\n\nFor protein graph, we use pre-trained ESM-2 features to initialize. For ligand graph, no pre-trained features are used. The node feature is a 56-dimensional vector related to atom, degree, etc. More details about features in protein and ligand can be found in Appendix Sec 1.3.\n\n> **Q7: Edge features not included**\n\nIn graph learning, the use of edge features is not universally adopted. Some methods incorporate edge features, while others not. There is no consensus in scientific community about which approach is superior. In our study, we follow TankBind and E3Bind that do not use edge features. We are open to explore edge features in future work."}, {"review_id": "oSg2hmSW8A", "reviewer": "Reviewer_oHVp", "summary": "In this paper, a protein-ligand binding algorithm based on the E3NN framework, named FABind, is proposed for docking molecules and proteins. The algorithm firstly predicts the pockets on the interface of the proteins, and then utilizes the predicted pockets to dock with the molecule ligand, and the algorithm results show that FABind demonstrates superior performance to TANKBIND, E3BIND, and other methods on protein-molecule docking datasets.", "questions": "\n1. In the right of Figure 1, ligand is n*3, receptor is n*3, but pair representation is n*m*3, the dimension is a little bit confusion.\n\n2. can FABind solve atom clash？\n\n3. what the performance on pocket prediction?\n", "limitations": "\n1. No release code\n\n2. no wet-lab experiments\n", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "\n1. predict pocket in docking framework is reasonable. \n\n2. The experimental results show that FABind outperforms TANKBIND, E3BIND and realizes a new sota.\n", "weaknesses": "1. FABind used EGNN to update atom coordinates, although EGNN is equivariant neural network. however EGNN doesn't maintain rigid constraint for protein. It's the major weakness when using EGNN on rigid docking problem.\n\n2. it's not clear why the neural network need a separate interfacial message passing module because you have already use cross attention neural network. you can use attention as a soft interface.\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your thorough review and invaluable suggestions. We provide the following responses to address your concerns.\n\n> **W1: FABind used EGNN to update atom coordinates, although EGNN is equivariant neural network. however EGNN doesn't maintain rigid constraint for protein. It's the major weakness when using EGNN on rigid docking problem.**\n> \n\nThank you for pointing out the potential limitation of using EGNN for rigid docking problems. We recognize that this might be a theoretical challenge. However, in our implementation, we employed a masking strategy where we only update the ligand's coordinates and keep the protein's coordinates fixed during each pass through the model. This ensures the rigidity of the protein structure. We will add detailed description of this in the later version.\n\n> **W2: It's not clear why the neural network need a separate interfacial message passing module because you have already use cross attention neural network. you can use attention as a soft interface.**\n> \n\nThank you for highlighting the potential role of cross attention in extracting a soft interface. In our work, we choose to incorporate an interfacial message passing module for the following reasons:\n\n1. **Focused Updating**: By constructing a hard interface, we can update independent and interfacial structures separately. Utilizing cross attention alone for soft interface updating might lead to the model altering structures far from the contact surface. This is not desirable in our case, as the docking process mainly occurs at the contact surface. Our approach allows for more targeted updates that concentrate on the key areas involved in docking.\n2. **Dynamic Structure Maintenance**: While we dynamically update the structure, the hard interface is also updated accordingly. By stacking the interfacial message passing layer multiple times, we force the docking module to dynamically capture the interaction. \n\n> **Q1: In the right of Figure 1, ligand is n*3, receptor is n*3, but pair representation is n*m*3, the dimension is a little bit confusion.**\n> \n\nSorry for the inconsistency in Figure 1, where the receptor should be represented as $m^3$, not $n^3$. Thanks for your careful review and we will correct this typo in the revised version.\n\n> **Q2: Can FABind solve atom clash？**\n> \n\nThank you for your question regarding the ability of FABind to solve atom clash.\n\n1. Within the ligand, atom clashes can be resolved through post-optimization. Specifically, post-optimization ensures that there are no clashes between atoms by constraining the distance between atoms by bond lengths. The performance of post-optimization is detailed in Appendix Table 2.\n2. We acknowledge the importance of addressing this issue. Though our loss function does impose a soft constraint on the distance between ligand atoms and protein alpha carbons, it cannot guarantee the complete absence of clashes between the ligand and the protein. This is also a limitation of most current models. We will explore methods to better handle this in the future work.\n\n> **Q3: What the performance on pocket prediction?**\n> \n\nWe have conducted the pocket prediction experiments and analyses in the paper Section 5.1. We compare our pocket prediction module with DL-based docking methods including TankBind [1] and E3Bind [2]. We also analyze the effect of combining ligand conformation and center constraint (regression loss) to the pocket prediction performance.\n\n**References**\n\n[1] Lu, Wei, et al. \"Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction.\" *Advances in neural information processing systems* 35 (2022): 7236-7249.\n\n[2] Zhang, Yangtian, et al. \"E3bind: An end-to-end equivariant network for protein-ligand docking.\" *arXiv preprint arXiv:2210.06069* (2022)."}, {"review_id": "1iXDSPvX6J", "reviewer": "Reviewer_N7PK", "summary": "This paper proposed a graph model named FABind for protein-ligand binding prediction. The major difference from existing work is that FABind integrate both protein pocket prediction and protein-ligand binding in a single model. The pocket prediction subtask is modeled as a binary classification problem, and the docking is a regression problem regulated with distance map based loss. The model was trained and tested using PDBbind v2020 data. Comparison with force-field based tools and GNN-based methods show the advantages of FABind. Furthermore, FABind's inference runs much faster than most of the benchmarks. Case studies are also provided in the main text and supplementary file.  ", "questions": "Please see limitation above. ", "limitations": "The authors should discuss more about the use of negative protein-ligand pairs in model fitting and evaluation. Also, different scenarios of protein pockets should be considered in evaluations.  ", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The major contribution of this work is the combination of protein pocket prediction and protein-ligand binding in one single GNN model.  paper is well written as well. I found it is very easy to follow. ", "weaknesses": "The protein-ligand complexes in training and test should be better explained. I am wondering whether negative protein-ligand pairs are used in both processes. Particularly, I am interested in seeing the performance for negative protein-ligand pairs. For protein pocket prediction, what would the model infer if a protein is undruggable, i.e.,  there is no native pocket on its smooth surface. In a different scenario, a protein could have multiple binding pocket; how would FABind behave in such cases? ", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful feedback. We take the opportunity to clarify the aspects you have raised.\n\n> **The protein-ligand complexes in training and test should be better explained.**\n> \n\nThe dataset details are in the appendix, we utilized the PDBBind dataset for our study. Each data sample provides the naturally docked complex structure. The training, validation, and test sets were split based on time, with the test set containing only complexes discovered in 2019 or later, while the train and validation sets use complexes before 2019. More details on this can be found in Appendix Section 1.1.\n\n> **I am wondering whether negative protein-ligand pairs are used in both processes. Particularly, I am interested in seeing the performance for negative protein-ligand pairs.**\n> \n\nOur approach models docking as a regression task, and evaluation metrics are the distance between the predicted and ground ligand atoms/center, such as RMSD and Centroid Distance. Unlike classification tasks, constructing negative examples through a random combination of ligand and protein is not feasible in this context, as determining the ground truth for randomly combined pairs is ambiguous, and different data samples are not in the same coordinate system.\n\n> **For protein pocket prediction, what would the model infer if a protein is undruggable, i.e., there is no native pocket on its smooth surface.**\n> \n\nThanks for this question, which is a really interesting point. We explored this by selecting an undruggable target, Myc [1], known for features that render it undruggable, such as the lack of medicinally binding pockets and its relatively smooth surface. We performed inference on this example by randomly selecting some ligands from PDBBind, and the results are included in Figure 1 of the uploaded PDF. We can see that FABind docks these ligands to a position but it is not a pocket. Therefore, the visualization would clearly show for undruggable protein, there would be no correct docking poses by our model.\n\n> **In a different scenario, a protein could have multiple binding pocket; how would FABind behave in such cases?**\n> \n\nIn nature, given a ligand, it may binds to different pockets of a protein. In this work, we mainly focus on the most probability binding pocket that a ligand would bind (the strongest binding strength) to, where the PDBBind dataset is also processed in this way. This is also the case we care mostly in real scenarios. \n\nThe scenario you mentioned mostly lies in the proteins that are symmetric or multimeric. In these cases, the ligand can equally bind to any of these symmetric pockets as correct one. We solve the majority of this issue by only keeping the protein chains which have an atom within a 10Å radius of any ligand atom. Such cases and the comparison with DiffDock [2] can be seen in Figure 2 of the uploaded PDF. Both our method and DiffDock can find the correct pocket. Besides, we can use external tools to recognize the symmetric proteins and post-process the results.\n\n**References**\n\n[1] He, Huiqin, et al. \"Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.\" *Stroke and Vascular Neurology* 5.4 (2020).\n\n[2] Corso, Gabriele, et al. \"Diffdock: Diffusion steps, twists, and turns for molecular docking.\" *arXiv preprint arXiv:2210.01776* (2022)"}, {"review_id": "WOSEDJcqSF", "reviewer": "Reviewer_5BP6", "summary": "This paper proposes an end-to-end model, FABind, which is a pocket prediction and docking end-to-end model considering the prediction accuracy and speed at the same time. By gradually adding the predicted pocket into the docking process, the model improves protein-ligand binding while minimizing differences between training and inference. An equivariant layer is also added in the model. According to empirical tests, FABind performs significantly better than the alternatives. ", "questions": "See above.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This work considers limitations for sampling-based methods and regression-based methods and combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.\n2. This approach employs a particular ligand to identify the precise pocket that the ligand binds to, as compared with conventional pocket prediction, which just uses protein as an input and predicts numerous probable pockets.", "weaknesses": "1. Why you choose 10 \\angstrom as the cut-off distance, have you tried other distances as the hyperparameter?\n2. The comprehensive training loss includes the pocket losses and docking losses, which each have two different kinds of losses, there are many weight hyperparameters that should be determined, how you choose the weight hyperparameters? The pocket prediction and docking seem to be two different tasks? Did you train only one model for these two tasks by the comprehensive training loss?\n3. In Table 1., the performance of the proposed model, FABind seems not good enough, as the DiffDock(40) has many superior results, what is the reason? But for the flexible blind self-docking, FABind performs much better than DiffDock, what cause these differences?\n4. You have pointed out the limitations for the sampling-based methods and regression-based methods, low efficiency and decreased accuracy, and the variation in protein sizes, I guess it may be better to show what is based on sampling-based methods, and what is based on the regression-based methods in the results, Table 1 and 2. Some models are tested on CPU, the comparison seems not fair, as you add the operation of refinement, which will increase the time. How you tackle the problem of the variation in protein sizes as you have mentioned?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your effort and the detailed questions about our work. We try our best to provide the following response to help address your concerns. \n\n> **W1: Cut-off distance**\n\nWe follow TankBind [1] and E3Bind [2] to set the cutoff to 10Å, which balances computational efficiency and the ability to capture relevant interactions. \n\nAccording to your suggestion, we conducted an ablation study to explore the impact of different cut-off distances. We experimented with 6Å, 10Å, and 14Å, and the results are shown in the Table 2 of the uploaded PDF. Our findings revealed that the 6Å cut-off resulted in the worst performance, while 10Å yields slight better results than 14Å. We opted for 10Å as the cut-off distance to enhance efficiency. \n\n> **W2: Loss weight hyperparameter and joint training**\n\nThanks for the detailed questions, the answers are as follows.\n\n1. **Selection of Weight Hyperparameters**: For the loss weight, we conducted a grid search around hyperparameters that allowed losses to be on a similar scale. Specifically, we fixed the weight for the pocket classification loss at 1 and searched for the pocket center constraint loss weight $\\alpha$ in the range of [0.1, 0.2, 0.3, 0.4, 0.5]. For the remaining hyperparameters $\\beta$ and $\\gamma$ in the docking loss, as well as the overall ratio between pocket loss and docking loss, we set the search range as [0.25, 0.5, 1.0, 2.0, 4]. This approach allowed us to find an optimal balance among the different components of the loss function. We choose the model with best valid performance for the final test.\n2. **Training Approach for Pocket Prediction and Docking**: In previous works, pocket prediction and docking tasks are separately trained. For instance, TankBind and E3Bind utilize external tools P2Rank for offline pocket prediction.  However, in our approach, we jointly trained one model (all with FABind layer) for both tasks using the comprehensive training loss as described in the paper Section 3.4. The model architecture (FABind layer) shared the same structure between the two parts, and the embeddings are connected, allowing the two components to mutually benefit from each other through joint optimization. In this way, we streamlined the process and achieved a seamless, end-to-end process for protein-ligand docking prediction.\n\n> **W3: Comparison with DiffDock**\n\nFor the DiffDock(40) [3] performance in terms of the <2Å metric, it's essential that DiffDock's confidence model is trained specifically for the classification task of whether the ligand RMSD is below 2Å or not. This specialized training is a reason why DiffDock performs better on this specific metric. Moreover, our FABind model exceeds the performance of DiffDock on 10 sampled poses with much higher efficiency.\n\nFor the second part, we think you mean that in the flexible blind self-docking on unseen receptors experiment, FABind performs much better than DiffDock. We attribute this result to the following aspects: \n\n1. We have carefully modeled pocket prediction module, achieving excellent pocket prediction through jointly optimizing the pocket prediction and docking problems. Compared to DiffDock, our model is more accurate in pocket prediction (in Table 2 of the paper, Centroid Distance <2Å metric, FABind 57.6 % v.s. DiffDock(40) 43.4%) with better generalization, possibly contributing to the superior performance.\n2. The confidence model of DiffDock primarily focuses on optimizing ligand poses towards achieving <2Å accuracy. This specific focus allows DiffDock to excel in the <2Å metric, but it may not generalize well to unseen receptors.\n\nWe will add more discussion about this in the later version.\n\n> **W4: Classification of methods, time comparison, variation in protein sizes**\n\nThank you for raising these insightful questions, which we address as follows:\n\n1. **Classification of Methods**: To clarify the type of the methods used, we will include an additional column in Tables 1 and 2 to indicate whether each model is sample-based or regression-based. Briefly speaking, among the DL based methods, only DiffDock is sample-based methods while the others are all regression-based methods.\n2. **Computational Time Comparison**: We strive for a fair comparison by utilizing GPU test where available and resorting to CPU only when GPU implementation is absent. Most of the traditional methods listed in the top half of the table do not have GPU implementations, so CPU testing is the only choice. Further, we have demonstrated the difference in computational speed due to iterative refinement in Appendix Table 3. This clearly shows that the refinement operation does not compromise FABind's status as a fast approach. Besides, we follow previous works such as TankBind and E3Bind that the comparison is the same as what they adopted. \n3. **Handling Variation in Protein Sizes**: PDBBind dataset consists of a wide variation in protein sizes, ranging from 28 to 3012 amino acids. This variation, if fed directly to the docking module, could pose challenges in capturing key contact surface information and would consume significant GPU memory. Our solution involves introducing a pocket prediction module. Regardless of the whole protein's size, the docking module only models the pocket part, consisting of a small number of amino acids, such as a little over a hundred. Models that perform docking directly on the whole protein, such as Equibind and DiffDock, can lead to substantial computational costs for larger proteins.\n\n**References**\n\n[1] Lu, Wei, et al. \"Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction.\" *Advances in neural information processing systems* 35 (2022): 7236-7249.\n\n[2] Zhang, Yangtian, et al. \"E3bind: An end-to-end equivariant network for protein-ligand docking.\" *arXiv preprint arXiv:2210.06069* (2022).\n\n[3] Corso, Gabriele, et al. \"Diffdock: Diffusion steps, twists, and turns for molecular docking.\" *arXiv preprint arXiv:2210.01776* (2022)\n"}, {"review_id": "njs7W3HX3j", "reviewer": "Reviewer_Vntv", "summary": "The paper presents a new method for protein ligand docking based on regression of the pocket center and the ligand atomic positions. Although the none of the components of is highly novel, they are well justified and tied together providing strong empirical performance with very fast inference speed. Although there are some adjustments to be made during the rebuttal process (see below) once these are completed I believe the paper will be ready for acceptance.", "questions": "There are some critical steps of explanation of the method that seems to be missing from both paper and appendix, for example:\n1. How is the ligand position initialized and placed during docking? During the pocket prediction since there are message-passing layers between the ligand and the receptor how are they placed relative to one another? During docking how are the ligand nodes initialized during docking? For both: is equivariance to the initial orientation of the receptor guaranteed?\n2. Do the layers in the docking and pocket prediction share any parameters? Are the gradients propagated during training between the pocket and docking components? If no to both questions, why is the training of the two components joint (a single loss function) and not separate?\n\nUnclear components in the evaluation:\n1. In the appendix 1 it is mentioned that approx. 500/19k complexes are further removed because of the ligand size or number of contacts. Does this filtering also affect the test set? \n2. Appendix 2: how is the alignment between the ESMFold and the crystal structures performed?\n3. Some of the baselines have reported different numbers compared to their peer-reviewed manuscripts (e.g. TANKBind, DiffDock), is it because of a slightly different test set? If not I recommend the authors to clarify the situation with the authors of the respective publications. \n4. A detailed description of the hyperparameter selection process should be added to the supplementary material", "limitations": "I was not able to run the provided code to reproduce the results of the paper due to a lack of documentation.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Although most of the main components of the method were present in previous regression based docking methods, the authors tied these together adding a few key ingredients (e.g. center constraint, pairwise distance loss, cross attention) managing to significantly increase the performance over previous methods.", "weaknesses": "Main weaknesses:\n1. Some parts of the method section lack clarity (see next section) and should be improved.\n2. As FABind selects the pocket with a regressive approach (unlike for example TANKBind) it seems that it is critically affected by uncertainty such as symmetries in the receptor, as the residues of the symmetrically opposite pockets would receive the same likelihood and a random independent selection of these would cause the predicted pocket center to be approximately in between the two symmetric pockets. These will apply also in other settings where symmetries are not present but the ligand simply has multiple binding sites. These were artificially removed by the test setting by EquiBind (taking only chains within 10A, not possible in reality without knowing already the binding pocket) but are often present in real blind docking scenarios.\n3. Although the method is repeatedly presented as end-to-end, the approach seems to be disjoint with the pocket and docking phases with separate losses that are (almost) independent\n\nSome claims need to be adjusted:\n1. line 72: the authors claim improvements in performance based on the mean ligand RMSD metric (the only place in the manuscript where this metric is actually highlighted), a metric which is known not to be reflective of docking performance.\n2. line 341: “FABind outperforms existing methods by a large margin with higher efficiency.” “large margin” in performance seems an unjustified claim based on the results (in many cases on par or worse than DiffDock or other methods)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and the review, and the insightful comments. We provide the following responses to your concerns. \n\n> **W1&2: Multiple binding sites**\n\nThanks for pointing out this important factor. We acknowledge the potential challenges with multiple pockets in the receptor.  In current process, we follow EquiBind to solve the majority of this issue by only keeping the protein chains which have an atom within a 10Å radius of any ligand atom. We show some cases with symmetric proteins in Figure 2 of the uploaded PDF, in which both our method and DiffDock can find the correct pocket. In addition, we can also use external tools to recognize the symmetric proteins (even without knowing the pocket information) in advance and process it by only keeping one of its symmetric parts as input. We want to emphasize that our method is primarily designed to predict the pocket and ligand pose where the binding strength is strongest, which aligns with the data perspective of the PDBBind dataset that emphasizes the strongest natural binding pose to a given site. While symmetries and multiple binding sites may pose challenges, our method reflects the natural objective of identifying the most probable binding configuration. Besides, we would like to highlight: (1) comparison with sample methods, although we may not model the distribution of binding sites like sample methods, validating these sites in practice is also not easy; (2) our method achieves fast speed with also good performance, this is a crucial factor in large-scale virtual screening. \n\n> **W3 & Q1.2: End-to-end training for both pocket prediction and docking tasks**\n\nSorry for any confusion. We indeed jointly trained one model (all with FABind layer) for both pocket prediction and docking tasks using the comprehensive training loss as described in the paper Section 3.4. The model architecture (FABind layer) shared same structure between the two parts (but not share parameters), and the embeddings are connected. Therefore, the gradients can propagate between these two components, allowing for mutually benefiting from each other. In this way, we streamlined the process and achieved a seamless, end-to-end process for protein-ligand docking prediction.\n\n> **Adjust claims**\n> \n\nThanks for pointing out these inappropriate claims. We will adjust them in the revised version.\n\n> **Q1.1: Ligand position initialization and equivariance**\n> \n\nWe first use Merck Molecular Force Field (MMFF) in RDKit to generate random low-energy ligand conformation, denoted as $C_1$ . Then we apply uniform random rotation on $C_1$ and get $C_2$. Then, for the pocket prediction module, we translate $C_2$ to the center of the protein (i.e., origin of coordinates as we move the protein to the origin of coordinates) to avoid pocket information leakage. For the docking module, we translate $C_2$ to the predicted pocket center by the pocket prediction module.\n\nThe equivariance to the initial orientation of the receptor is also guaranteed. For the pocket prediction, as described in paper Section 3.3, we use invariant features (logits) from FABind layer to classify each residue as either belong or not belong to the pocket. Then we calculate the center of these classified residues and take it as the predicted pocket center. So if the initial receptor is translated or rotated, the predicted pocket center will also be translated or rotated accordingly, ensuring the equivariance. For the docking module, the equivariance is naturally guaranteed by the FABind model as it is a variant of EGNN.\n\n> **Q2.1: Data filtering**\n> \n\nThis filtering doesn’t affect the test set. It only filters some complexes from the training set.\n\n> **Q2.2: Alignment between the ESMFold and the crystal structures**\n> \n\nThanks for this question. We follow the DiffDock to align the ESMFold predicted structure to the crystal structures to obtain the “ground truth” docked structures for apo-structure docking experiments. More details can be found in DiffDock Appendix Section D.2. Briefly speaking, the alignment process involves using the Kabsch algorithm with exponential weights to align receptors' residues. For each receptor, a weight is assigned based on the minimum distance to a ligand atom, with closer residues receiving higher weight, and the smoothing factor $\\lambda$ is individually selected within the range [0, 1] to best preserve distances. The L-BFGS-B method is used to minimize the $\\lambda$.\n\n> **Q2.3: Reported baseline performance**\n> \n\nThanks for pointing out this. There is no difference in the test sets between the compared baselines and our work. The results of Table 1 and 2 mainly come from the E3bind, including traditional docking methods: QVina-W, GNINA, SMINA, GLIDE, Vina, as well as DL-based methods: EquiBind, TankBind, E3Bind. For the DiffDock results, as the results of sample-based method are unstable (both issues from DiffDock github and our experiments have found this), we reproduce the results using its pre-trained checkpoint. Specifically, we run the DiffDock inference codes with three random seeds, and report the average results.\n\n> **Q2.4: Hyperparameter selection**\n> \n\nThanks for the question. To clarify, some hyperparameters were set following previous works to ensure consistency and comparability. For the remaining hyperparameters, we employed a grid search method to explore the optimal values that would yield the best performance. We will include a comprehensive description of the hyperparameter selection process in the supplementary material.\n\n> **L1: Reproductivity**\n> \n\nSorry for the lack of documentation that hindered to run the provided code. We have made a documentation to run the code, and updated the code to AC according to the rebuttal rule. Please contact AC if needed."}]

Author rebuttal:
Dear Reviewers,

We would like to extend our heartfelt gratitude to each one of you for the time, effort, and expertise you have invested in reviewing our paper. Your insightful comments and constructive feedback have been instrumental in enhancing the quality of the research.

In response to your comments and to provide further clarity on some of the points raised, we have prepared additional results shown in figures and tables. Please find the uploaded PDF file and refer to the specific figures and tables as needed. 

Once again, thank you for your valuable contributions to this work.

Sincerely,

Authors

Meta-review: All reviewers acknowledge the comparable performance of the proposed method with recent sota on protein-ligand binding while being computationally more efficient. The improvement is due to a novel combination of existing methods as well as a few new tricks. The AC agrees with the unanimous ratings to accept as the paper has clear merits for an important task and thus should be disseminated.
Decision: Accept (poster)