Fast and Accurate Blind Flexible Docking
Authors: Zizhuo Zhang
Lijun Wu
Kaiyuan Gao
Jiangchao Yao
Tao Qin
Bo Han
Venue: ICLR 2025 Poster (2025)
Keywords: Blind Flexible Molecular Docking
Structure Prediction
AI4Science
Ligand-Protein Graph
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: This paper proposes FABFlex, a fast and accurate regression-based multi-task learning model designed for realistic blind flexible docking scenarios.

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

Published as a conference paper at ICLR 2025
FAST AND ACCURATE BLIND FLEXIBLE DOCKING
Zizhuo Zhang' Lijun 2u Kaiyuan Gao Jiangchao Yao Tao Qin
Bo Han I
TMLR Group, Department of Computer Science, Hong Kong Baptist University Shanghai AI Laboratory HHuazhong University of Science and Technology ACMIC, Shanghai Jiao Tong University 'Micrsoott Research A14Science (cszzzhang, bhanml 1@comp hkbu edu. hk, wwwtersww@gaiil.coomm com im ahusthhht...... wwu.tteeu......... cn, Sunarker jtu. edu. cn, taocinemicroosoft.ccom
ABSTRACT
Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets. plays vital role in drug discovery. However, exist- ing docking methods often face limitations: they either overlook crucial struc- tural changes by assuming protein rigidity or suffer from low computational ef- ficiency due their reliance on generative models for structure sampling. To address these challenges, we propose FABFlex, a fast and accurate regression- based multi-task learning model designed for realistic blind flexible docking sce- narios, where proteins exhibit flexibility and binding pocket sites are unknown (blind). Specifically, FABFlex's 's architecture comprises three specialized modules working in concert: (1) A pocket prediction module that identifies potential bind- ing sites, addressing the challenges inherent in blind docking scenarios. (2) A ligand docking module that predicts the bound (holo) structures of ligands from their unbound (apo) states. (3) A pocket docking module that forecasts the holo structures of protein pockets from their apo conformations. Notably, FABFlex incorporates an iterative update mechanism that serves as a conduit between the ligand and pocket docking modules, enabling continuous structural refinements. This approach effectively integrates the three subtasks of blind flexible dock- ing-pocket identification, ligand conformation prediction, and protein flexibility modeling-ing intiin a unified, coherent framework. Extensive experiments on public benchmark datasets demonstrate that FABFlex not only achieves superior effec- tiveness in predicting accurate binding modes but also exhibits significant speed advantage (208x) compared to existing state-of of-the-art methods. Our code is re- leased at https: / thub. com tm1 wwrerrrrrurp/eeeeeeeeeooe /FO.
INTRODUCTION
Molecular docking is a pivotal technology in drug discovery, aiming at predicting the binding struc- tures of -pottcin complexes elucidate how drug-like small molecules (ligands) interact with target proteins (Morris et al., 1996; Morris & Lim- Wilby, 2008; Agarwal & Mchrotra, 2016). Over the past decades, the development of molecular docking has seen continuous breakthroughs, evolv- ing from traditional simulation software grounded in the principles of physics and chemistry to recent deep learning-based methods (Crampon et al., 2022). Traditional methods typically utilize empirical energy scoring functions to rank numerous searched conformations (Trott & Olson, 2010; Friesner et al., 2004; McNutt et al., 2021; Koes et al., 2013), often resulting excessive processing times and heavy computational burdens.
The shift towards deep lcarning-based docking approaches represents a significant transformation in the field, offering an alternative pathway for exploring protein-molecule interactions. Despite technological advances, a substantial number of these methods (Lu et al., 2022; Zhang et al., 2023; Stärk et al., 2022; Zhou et al.; Pei et al., 2024: Gao et al., 2024; Corso al., 2023) rely on the rigid docking paradigm. which assumes that proteins are rigid and remain static during the docking pro- cess. This simplification contradicts the physiological reality of molecular docking, where proteins
1 "Correponndence Bo Han Obhamm@@comp.hkhuu and Lijun Wu (apcterswu@ggaail.oo

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PDB 6HHJ
PDB 60IM
Protein
Apo Protein (AIphFoldZ)
Holo Ligand
Ligand Predicted by FABind
Figure 1: The two cases illustrate our motivation. These cases, involving PDB 6HHJ and PDB 60IM, highlight the structural discrepancy between apo proteins (AlphaFold2) and holo proteins. In the two cases, existing rigid docking method FABind (Pei et al., 2024) yields incorrect molecular docking results when apo proteins are inputted as direct substitutes for the original holo proteins.
exhibit sigmificant flexibility and dynamic behavior Wiiddmn- & Kern, 2007; Lane, 2023; Popovych et al., 2006), thereby restricting the practical applicability of these methods in real-world scenarios. Consequently, it is imperative to explore and develop flexible docking methods that more faithfully reflect the realistic docking process (Sahu et al., 2024).
The advent of AlphaFold series (Senior et al., 2020; Jumper et al., 2021; Abramson et al., 2024) has revolutionized the technology of protein structure prediction, enabling precise 3D protein structure predictions without relying on laborious methods such as X-ray crystallography (Ilri & Savino, 2008), or cryo-electron microscopy (Bonomi & Vendruscolo, 2019). Yet, integrating AlphaFold- predicted apo structures into existing docking workflows remains challenging. For instance, as two cases shown in Fig. 1, a notable discrepancy often exists between the Aiphaaold2-prediccee apo structures (Jumper et al., 2021) and the actual docked holo structure. Moreover, existing rigid docking methods (Lu et al., 2022; Pei et al., 2024) often yield wrong docking results when the Alphafoldd-prediccee apo proteins are used as direct substitutes for previously used holo protein.
Recent flexible docking methods (Lu et al., 2024; Huang et al.; Zhang al., 2024; Qiao al., 2024) predominantly rely on diffusion models and sampling strategy (Yang et al., 2023) due to the strong distribution modeling capability of generative models. For example, DynamicBind (Lu et al., 2024) utilizes equivariant geometric diffusion networks with multiple sampling iterations to reconstruct holo protein structures from AlphaFold2- based conformations. Though effective, the step-by-step diffusion process and extensive sampling requirements inherently reduce computational efficiency. In contrast, regression- bassdd methods (Lu et al., 2022: Zhang et al., 2023; Stärk et al., 2022), offer faster alternative by directly predicting the bound structures of protein-ligand complexes using well-designed neural networks. Notably, regression-basse for flexible docking scenarios. Within this category, the FABind series (Pei et al., 2024; Gao et al., 2024) is committed to balance docking accuracy with computational efficiency through a multi- task model. However, the FABind series still adheres to the rigid protein assumption, limiting its efficacy in realistic flexible docking scenario. Consequently, the challenge of developing a method that combines both accuracy and speed for blind flexible docking remains largely unexplored.
approaches have not yet been explored
In this study, we propose FABFlex, regression-based multi-task learning model crafted to achieve both Fast and Accurate Blind Flexible docking. Specifically, FABFlex consists of three designed modules, each targeting a subtask decomposed from the process of blind flexible docking: (1) a pocket prediction module that identifies binding pocket sites; (2) ligand docking module that pre- dicts the structure of holo ligand; (3) a pocket docking module that predicts the structure of holo pocket. Each module is built using an E(3)-eiuuvaaiant graph neural network layer, called "FABind layer" (Pei et al., 2024), which is tailored to handle the ligand- ligand-protein heterogeneous graph. The operational pipeline of FABFlex begins with the pocket prediction module, which determines the protein residues constituting the binding pocket, effectively navigating the challenge of the blind setting. Subsequently, the ligand and pocket docking modules leverage the predicted pocket in- formation to predict the holo structures of the ligand and pocket, respectively. An iterative update mechanism facilitates the exchange of structural predictions between the ligand and pocket docking modules, allowing for further coordinate refinements. Collectively, these three modules function in
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seamless end-to-end process, allowing FABFlex to simultancously predict the holo structures of both the ligand and pocket in a single operation, thus ensuring faster computation.
We conduct experiments on public docking benchmark PDBBind to evaluate FABFlex against a variety of docking methods. Compared to the basclines, FABFlex effectively increases the per- centage of ligand RMSD below 2 to 40.59% and reduces the pocket RMSD to 1.10Å. Notably, FABFlex significantly accelerates the computational speed, approximately 208 times faster than the recent state-of-the-an flexible docking method, DynamicBind (Lu al., 2024).
RELATED WORK
Molecular docking, As a cornerstone of drug discovery, molecular docking, often synonoymous with ligand-protein docking, focuses the interactions between ligands and proteins. Traditional methods like Vina (Trout & Olson, 2010), Smina (Koes et al., 2013), Glide (Friesner et al., 2004), Gnina (McNutt et al., 2021) and Gold (Jones al., 1997), use physics-based scores to analyze these interaction, which. though effective, tend to be computationally intensive. Recent progress in geometric deep learning has sparked the development of deep learning-basee docking strate- gies (Crampon et al., 2022), which can be broadly categorized into gegression-basee and sampling- based methods. Regression-basee 2022), E3Bind (Zhang et al., 2023) and FABind (Pei et al., 2024) leverage various geometric neu- ral networks to directly predict binding structures. Conversely, sampling-bane
methods like EquiBind (Stärk et al., 2022), TankBind (Lu al.,
methods like Diff- Dock (Corso et al., 2023), manipulate the rotation, translation and torsion of ligands using diffusion models. These methods generally simplify the problem by assuming protein rigidity, neglecting the dynamic nature of protein in realistic docking scenarios.
Flexible molecular docking. Recent methods in flexible docking, such as DynamicBind (Lu et al., 2024), ReDock (Huang al.), PackDock (Zhang et al., 2024) and NeuralPLexer (Qiao al., 2024), are primarily based on diffusion models with sampling strategy. For example, DynamicBind (Lu al., 2024) uses equivariant geometric diffusion networks reconstruct the holo structures of both ligand and protein from their apo states. ReDock (Huang et al.) adopts the neural diffusion bridge model that employs mnergy-to-geometie mapping on geometric manifolds to predict rrotenn-lggand binding structures. While these methods effectively enhance the docking performance, they suf- fer from the typical flaws associated with diffusion models and multi-round sampling strategy, i.c., low computational efficiency. This limitation impedes the scalability of these methods in assessing extensive volumes of potential, unknown molecule-protein interactions, which are crucial for ad- vancing drug discovery. In this paper, we attempt to provide regression-based solution aimed at achieving both fast inference and high docking accuracy.
METHODOLOGY
3.1 PROBLEM STATEMENT AND PRELIMINARY
Notations. Each ligand-protein complex is denoted as heterogeneous graph G EV = (V*, EP), == (E), Ed, 8.)),. where V and & denote the sets of nodes and edges, respectively. Specifically, in the ligand subgraph g (V),E'), each node e = (h;, x;) E Vi corresponds an atom of ligand, with h, E Rd representing the pre-extrated feature by TorchDrug (Zhu et al., 2022) and X; E IR3 specifying its spatial coordinate. The edge set El encompasses the chemical bonds within the ligand. In the protein subgraph GD (V², VP,,, each node fi = (h,,x;) E VP represents residues, with h, Rd derived from ESM-2 (Lin et al., 2022) as the pretrained feature, and x, E 3 indicating the coordinate of the C. atom in the residue. The edge set Ed connects residues that are within an 8Á distance. Additionally, the set of external interface edges, denoted as Elp, comprises edges that connect nodes from the ligand set J to the pro- tein set VP when they are spatially within 10Å of each other. When specifically focusing on the true pocket region, the ligand-pocket complex is depicted as a reduced heterogeneous graph ",(,, _(P")), where DPs E and Elps are derived from the subsets of the protein that constitutes the pocket. Similarly, a hat is used in the notation represent the predicted pocket, G' (v> = (V1, Ver), = (E), Ep=, [Epp)))
g = {V* : (V', ) E := (E',ET,
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Bound Complex
Holo Ligand
Holo Pocket
FABind Layers N
FABind Layers N
Ligand Docking Module
Pocket Docking Module
Iterative Update Mechanism
Predicted Pocket
Pocket Prediction Module
DOOOOOOOOOO
FABind Layers x M
Apo Ligand
Apo Protein
Figure 2: The overview of proposed FABFlex model, which consists of pocket prediction module, ligand docking module, and pocket docking module. The pocket prediction module identifies pocket residues within the protein. Based on the predicted binding pocket sites, the ligand docking module and pocket docking module predict the holo structures of the ligand and pocket, respectively. An iterative update mechanism facilitates the exchange of predictions between the ligand and pocket docking modules, enabling further coordinate refinements. These modules work together within unified andtto-end model for the blind flexible docking scenario, i.c., "(apo protein, apo ligand) > (holo pocket, holo ligand)".
Blind Flexible Docking, Given an unbounded pair of conformations: an apo ligand randomly ini- tialized by RDKit (Landrum al., 2013) and an apo protein predicted by AlphaFold2 (Jumper et al., 2021), the aim of blind flexible molecular docking is to predict the bound structure of ligand-pocket complex, i.e., holo ligand and holo pocket, denoted & [{&: (&Khsees hisss', (&))) J. where n' = | and n* p indicate the number of ligand atoms and pocket residues respectively. Unlike most existing studies that assume protein rigidity (Pei et al., 2024) or rely on pre-known pocket sites (Zhang et al., 2024), the blind flexible setting reflects the challenges encountered in real-world molecular docking scenarios.
Fundamental Component: Bind Layer FABind (Pei et al., 2024) and enhanced version, FABind+ (Gao et al., 2024), establish an end-to-end deep learning framework that simultancously predicts binding pocket sites and holo ligand structure under rigid setting. The fundamental ge- ometric graph neural network used in both approaches is called "FABind layer", denoted as 3(.), which is an improved Equi-eaui graph neural networks (EGNN) (Satorras et al., 2021) tailored for ligand-protedd complex graph. The 1-th FABind layer is denoted follows:
(1+1) h h (1+1)
(I+1)
(1+1) (1+1) X Pij = 3(h .h
X; P:P).
(1)
where Pij E Rd is the pair embedding of the ligand-protein node pair (vi, Uj) D' VP, and the hidden size. Similar to general graph neural networks (Wu et al., 2020), we can stack multiple FABind layers to extract deeper features and capture high-order information within the graph. Inspired by the fast inference of the FABind series methods, we employ the FABind layer as our fundamental component in constructing each module of our FABFlex.
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3.2 FABFLEX
3.2. I
DESIGN PHILOSOPHY
The design philosophy of FABFlex centers achieving both high efficiency and accuracy in blind flexible docking task, which can be decomposed into three key subtasks: identifying pocket sites, predicting the bound holo structure of ligand, and predicting the bound holo structure of pocket. Specifically, the first subtask can be modelled as a binary classification problem to determine which protein residues form the docking pocket, while the other two subtasks are 3D coordinate regression problems. To pursue faster docking computation. the FABFlex model is designed to predict the docking results in single-pass operation, without requirements of extensive sampling and repetitive computations. Additionally, FABFlex aims to operate without relying on external tools that could add extra computational overhead, such the pocket detection tool P2Rank (Krivák & Hoksza, 2018), which is used in existing studies (Lu et al., 2022; Zhang et al., 2023) to detect candidate pocket sites. According above philosophy, the FABFlex model builds on an end-to-end multi-task learning framework tailored for blind flexible docking.
3.2.2 ARCHITECTURE OF FABFLEX
An overview of the proposed FABFlex model is illustrated in Fig. 2. Designed to tackle the intricacy of blind flexible docking. FABFlex employs collaborative architecture consisting of three special- ized modules: (1) a pocket prediction module, denoted as (s(.); (2) a ligand docking module,
denoted as (M)+):: (3) pocket docking module, denoted Mr (-) All modules are implemented using stacked FABind layers, while each tailored to specific subtask decomposed from blind flex- ible docking. Specifically, the pocket prediction module responsible for identifying the residues that form the pocket, addressing the "blind" issue, as formulated below:
fushsssm MMsg, {h, x, Jisig', (h,.8) (h),sw))))))) {usheısar- = (ū, @ ushissnn E Dop
(2)
where g is the ligand-protein graph, (ū, hsisn (ūj E 13) is an indicator vector predicted by the pocket prediction module to locate the pocket sites, Dor denotes the predicted set of pocket residues and ~M = |'P1, J. O symbolizes the selection operation based on the indicator vector. The predicted pocket sites by pocket prediction module enable the subsequent ligand and pocket docking modules to concentrate effectively on the crucial pocket area, narrowing the large ligand -eantdn graph to a more targeted ligand pockett graph.
The ligand docking module and the pocket docking module cater to the "flexible docking" by pre- dicting the bound structures of holo ligand and holo pocket respectively, formulated follows:
(iihe = Mr(g", (hi, xihisgat, (hj, Xihisssan)), = (&
(3)
(h,, xihissw', (h3, xj xi)isss0=)),
where g" is the ligand-pocket graph, (& and (&t hessar are predicted structures of ligand and pocket respectively. In current model, the ligand docking module and the pocket docking module predict the structures in isolation, which has gap to reflect the interactive influence between ligand atoms and pocket residues in docking process. To rectify this, there is need for a bridge that connects the ligand docking module and pocket docking module, enhancing the model to capture the nature of interactive dynamics of docking.
3.2.3 ITERATIVE UPDATE MECHANISM
To facilitate the exchange of predicted structures between the ligand docking module and the pocket docking module, we introduce an iterative update mechanism to further promote the coordinate refinement. Specifically, the predicted ligand and pocket, which together form the updated ligand- pocket graph, are fed back into their respective modules to produce new predictions. This k-th iterative process can be formulated follows:
(x( (1+1) hiseww = Mr(((()+ (h,, R! Jisesw (h3, & Hisssar),
(4)
(x(++1) J = Mr(g())) (h, x{(²) heigas (hy, x hisgar=);
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where ((k)- denotes the ligand-pocker graph updated by the k--th iteration's predicted coordinates of ligand atoms {x((A) Jisign and pocket residues (x(*) hissgner, and the number of iterations K is a hyperparameter discussed in Appendix C.5. Notably, the update mechanism iterates solely on the coordinates, excluding the features. Additionally, inspired by AlpahFold2 (Jumper et al., 2021), we only record the gradient at the final iteration during training process. These strategies are beneficial in reducing the memory demands and easing computational burden.
3.2.4 TRAINING LOSS AND PIPELINE
Training Loss. FABFlex is built on a multi-task learning framework, utilizing multiple losses to supervise different modules from various aspects. Referring FABind series (Pei al., 2024; Gao et al., 2024), the training loss comprises pocket prediction loss, ligand coordinate loss, pocket coordinate loss, and distance map constraint loss, formulated as follows:
= a Spocket + 0r2 Ligand,coord + 0'3 Spockst.coodd + a4 Cd
(5)
where the pocket prediction loss ppocket peod encompasses a residue classification loss and a pocket center loss. The ligand coordinate loss Lligand coord is Huber regression loss to measure the distance between predicted and ground-truth coordinates of ligand atoms. Similarly, the pocket coordinate loss L-oocketcoood coord is Huber loss to compute the coordinate distance between predicted and ground- truth pocket residues, The distance map loss Ldis.mop supervises the predicted relative distance of atom-residue pairs. The details of implement of training loss are provided in Appendix A.I.
Pipeline. Given an apo ligand initilized by RDKit and an apo protein predicted by AlphaFold2, the ligand is initially positioned the center of the protein construct the ligand-protein graph G. This graph is passed through the pocket prediction module to identify the binding pocket residues (uz "ysass. Navigated by the predicted pocket, the ligand is translated from the protein center to the pocket center, creating the ligand-pocket graph G". This graph is then fed into the ligand and pocket docking modules, where it undergoes refinement through iterative update mechanism. At the final iteration, the predicted holo structures of ligand { and pocket (& are obtained from their respective docking modules. The pscudo code provided in Appendix A.2.
Notably, we adopt a partial teacher-forcing (Lamb et al., 2016) strategy during training process. When passing pocket information from the pocket prediction module to the docking modules, probability factor is set to control whether the true pocket sites (with probability p) or the predicted pocket sites (with probability model to rely partially on ground-truth pocket sites for stability, while progressively learning to depend on its own pocket predictions.
p) are passed to the docking modules. This strategy allows the
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTING
Dataset Construction. Our experiments are conducted on the widely used public PDBBind v2020 dataset', which contains a comprehensive collection of 19,443 protein-ligand crystal complex struc- tures with experimentally measured binding affinities. For each complex, we align the protein com- ponent with its corresponding AlphaFold2-prees preddccee structure to obtain the apo protein conforma- tions. To ensure consistency with previous work (Pei et al., 2024; Lu et al., 2024), we employ the same dataset splitting and adhere to similar data preprocessing steps. Specifically, complexes deposited before 2019 are utilized as the training set (12,807 complexes) and validation set (734 complexes), while those recorded after 2019 are designated as test set (303 complexes). Additional data preprocessing details are provided in Appendix B.I.
Baselines. We compare our proposed FABFlex against a spectrum of competitors, which are cate- gorized into traditional docking software and deep learning docking methods. Within the traditional software category, we compare against well-established software including Vina (Trott & Olson, 2010), Glide (Friesner et al., 2004) and Gnina (McNutt et al., 2021). For deep learning- based meth- ods, we include Bin (Lu et al., 2022), FABind (Pei et al., 2024), FABind+ (Gao et al., 2024),
http://pdhbind.orr.cm/

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Table 1: Ligand performance comparison of blind flexible docking.
Ligand RMSD
Method
All Cases
Unseen Protein Receptors
Average Runtime (s)
Percentiles
Below
Percentiles
% Below
2.5%
50%
75%
Mean
2A
< 5
25%
50%
75%
Mean
2A
< &5
Traditional Docking Software
Vina
4.79
7.14
9.21
7.14
6.67
27.33
5.27
7.06
8.84
7.15
6.25
23.21
205'
Glide
2.84
5.77
8.04
5.81
14.66
40.60
2.38
5.01
7.17
5.21
21,36
49.51
1405'
Gnina
2.58
5.17
8.42
5.76
19.32
48.47
2.03
4.96
7.35
5.33
24.55
50.91
146
Deep
Learning based Rigid Docking Method'
TankBind
2.82
4.53
7.79
7.79
8.91
54,46
2.88
4.45
7.53
7.60
4.39
58.77
0.87
FABind
2.19
3.73
8,39
6.63
22.11
60.73
2.73
4.83
9.35
7.15
8.77
50.88
0.12
BABind+
1.58
2.79
6.64
5.63
35.64
66.01
1.93
3.13
8.59
27.19
57.89
0.16
DifiDock
1.82
3.92
6.83
6.07
29.04
60.73
1.97
4.82
8.03
7.41
26.32
51.75
82.83
DifiDock-L
1.55
3.22
6.86
5.99
36.75
62.58
1.86
3.16
9.09
7.14
29.82
61.40
58.72
Deep
Learning-hased Flexible Docking Methods
DynamicHind
3.16
7.14
6.19
33.00
64.69
2.23
4.02
10.23
20.18
102.12
FABFlex
1.40
2.96
6.16
5.44
40.59
68.32
1811
3,51
8.03
7.17
32.46
$9.65
0.49
Notes: The best resalts highlighted in buld, and the second best results underlined. The average runtime for each methou presented seconds. The asterisk (*) indicutes that the method is exocuted on the CPU. The left part of the table compares ligand RMSD on all test cases, while the right part provides more rigorous comparison for those cases involving protein recoptors that were unseen during training process.
DiffDock (Corso et al., 2023), DifiDock-L (Corso et al., 2024) and DynamicBind (Lu et al., 2024). Among these, TankBind, FABind, FABind+, DiffDock and DiffDDok-L. maintain the rigid protein assumption, while DynamicBind is recently published flexible docking method. From another perspectives, Tank FABind and FABind+ are rooted in regression hhased approaches, whereas DifiDock, DiftDock-L and DynamicBind employ sampling- based strategy with diffusion models to refine the conformations of proteins and ligands. Further details are provided in Appendix B.2.
Evaluation Metrics. Our primary evaluation metric is the root-mean-square deviation (RMSD) of Cartesian coordinates, which assesses the accuracy of predicted versus ground -tuuh structures for both ligands (Ligand RMSD) and binding pockets (Pocket RMSD). These metrics reflect the capability of docking model in accurately predicting ligand structures at atom level and pocket structures at residue level. For Ligand RMSD, we report the percentile values (25%, 50%, 75% and mean) and percentages of RMSD below the threshold (< 2 and < 5A).
Implementation Settings. The initial apo ligand structures are generated using the Experimental- Torsion Knowledge Distance Geometry (ETKDG) algorithm (Riniker & Landrum, 2015) in RD- Kit (Landrum al., 2013) from the molecules' SMILES sequences. The initial apo protein struc- tures are obtained through AlphaFold2 (Jumper et al., 2021) predictions based on the proteins' amino acid sequences. The more details of hyperparameter setting are provided in Appendix B.3.
4.2 LIGAND PERFORMANCE OF BLIND FLEXIBLE DOCKING
Table summarizes the comparison of ligand performance across various docking methods. The left part of the table performs the comparison on the all test cases. It can be observed that FABFlex consistently outperforms both traditional docking software and contemporary deep learning- based methods almost across all metrics. Typically, a prediction of a ligand's d's structure is considered suc- cessful if its predicted structure is within a RMSD of 2A from the true holo ligand structure (Lu et al., 2024). Thus, the ligand RMSD < 2A is a critical metric to evaluate the capacity of molecular dock- ing methods. FABFlex excels in this metric, achieving the ligand RMSD < 2A 40.59%, which has generated a significant margin compared to the second-best competitor, showcasing FABFlex's superior ability in predicting accurate binding structures of ligands.
The right part of Table performs a more rigorous assessment of docking methods using 1 14 ligand- protein complexes that involve protein receptors that were not seen during the training process. This assessment is crucial for evaluating the generalization capability of each method. Although FABFlex does not achieve the best results in every metric, it excels significantly in the critical measure of ligand RMSD < 2A. Notably, FABFlex reaches 32.46%, markedly outperforming all competitors, which fall below 30%. This performance underscores FABFlex's ability to generalize effectively to new and unseen proteins. Additional analysis of ligand performance is provided in Appendix C.I.
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Cumulative
Percentage (%)
1830
Inmulation Nertarraage (h)
430
Car
20.
250-
Dynamk:3ind (Mean 0.84)
DynomicBind 0.81)
FABFlex (Mean 1.30)
FABPle: (Nean = 0.85)
022
3.2
032
2.55
1.6
1.25
1.0
0.0
2.25
0.58
8.75
1.00
1:33
Pockect RMID
Pocket RMSD (A)
Figure Pocket performance comparison of blind flexible docking. The left figure shows the cumulative percentage of pocket RMSD on all test cases, while the right figure evaluates on those cases with protein receptors that were unseen during training process.
4.3 POCKET PERFORMANCE OF BLIND FLEXIBLE DOCKING
Fig. 3 illustrates the cumulative distribution of pocket RMSD performance. In the left figure, which evaluates all test complexes, we observe that although FABFlex does not outperform DynamicBind, still has positive effect refining pocket poses from the initial AlphaFold2 structures. The right figure focuses on complexes with unseen protein receptors. It can be observed that for pocket RMSD below 0.75A, the DynamicBind distribution curve almost always stays at the lowest position, while FABFlex maintains results that are not worse than those of AlphaFold2. This suggests that for these unseen protein receptors, FABFlex demonstrates better robustness compared DynamicBind, as it does not degrade the protein structures. These outcomes are not surprising, as DynamicBind employs a diffusion model to adjust the conformation the entire protein, rather than just the pocket region. While this approach reduces the degrees of freedom and enhances the accuracy, it may limit its ability to generalize to new proteins, and face computational efficiency challenges.
4.4 INFERENCE EFFICIENCY
High efficiency facilitates the widespread adoption of method in real-world applications. In the last column the right of Table 1, showcase comparison of the average inference time for each ligand-protein pair. Traditional docking software such as Vina, Glide, and Gnina exhibit no- tably longer inference times. Among regression- regression-based methods, TankBind, FABind, and FABind+ demonstrate considerably faster speed than sampling-based approaches such as DifiDock and Dy- namicBind. Notably, FABFlex achieves inference speed of only 0.49 seconds, which is approx- imately 208 times faster than DynamicBind, recently developed method for flexible docking that averages 102.12 seconds. This efficiency gain is attributed the design philosophy of FABFlex that directly predicts the bound structures without requiring multiple rounds of sampling or extra external pocket detection tools. Additionally, FABFlex specifically focuses on the conformational changes in the core pocket region, rather than the entire protein. As the pocket constitutes small part the protein, this design effectively reduces the computational burden.
Table 2: Performance of pocket prediction.
Given Apo Protcins
Given Holo Protcins
Method
Pocket Center (A)
Pocket Center (A)
CLS ACC
CLS ACC %
MAE |
RMSE
EucDist
MAE
RMSE 1
EucDist |
P2Rank
4.04
5.69
7.85
4.11
6.17
8.05
FABind
73.32
3,06
5.30
6.18
73.54 87.52
3,08
5.35
6,18
FABind+
87.18
3.49
5.09
6.94
3.50
5.13
6.69
FABFlex
87.08
3.29
4.83
6.59
87.06
3.31
4.89
6.63
Notes: "_"" means that P2Rank is an external pocket detection tool without pocket residue classification.
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therstve (Grooed
PDB 60IM
Iteration
Ieeration
Iteration
Ieeration
lteration 5
Iteration
Figure 4: Case PDB 60IM to intuitively present the process of iterative update.
4.5 POCKET PREDICTION ANALYSIS
Table 2 illustrates the performance of FABFlex's pocket prediction module in comparison with to existing P2Rank, FABind and FABind+. Among them, P2Rank (Krivák Hoksza, 2018) an open- source tool widely used in existing docking methods for pre-determining potential binding pocket sites (Lu et al., 2022; Zhang et al., 2023). To intuitively analyze the effectiveness of pocket prediction across different methods, We evaluate the quality of pocket prediction from perspectives: residue classification and pocket center position. For classification, we report the accuracy (CLS ACC). For (RMSE),
the pocket center, we employ the mean-absolute-erro (MAE), the cett-men--qquae--eeee and the Euclidean distance (EucDist), each calculated between predicted pocket center and native pocket center to quantify the accuracy of pocket localization. From Table 2, we can observe that regardless of apo or holo protein, FABFlex predicts pocket sites that are comparable to FABind and FABind+, and consistently outperform P2Rank. These results indicate the effectiveness of FABFlex in identifying pocket residues, even when confronting apo proteins. Additionally, integrating pocket prediction into the flexible docking process appears to be more advantageous than relying on an external pocket detection tool. Notably, FABFlex achieves the lowest RMSE at 4.83 and 4.89, and the lower RMSE suggests FABFlex's 's stability in error control, with fewer cases of extreme errors. Moreover, provide intuitive visualization of two pocket prediction cases in Appendix C.12.
Table 3: Experimental results of ablation studies.
Ligand RMSD (A)
Pocket RMSD (A)
Pocket Center (A)
Method
Mean
Median 4
<2A(%))
Mean
Median
MAE
RMSE +
EucDist
FABFlex
5.44
2.96
40.59
1.10
0.63
3.29
4.83
6.59
Docking Module
5.73
27.06
1.59
3.46
4.83
6.90
Iterative Update
6.14
3.78
19.80
1.11
0.63
3.33
4.88
6.67
Iterative Internally
5.35
2.94
35.31
1.42
0.88
3.36
4.86
6.73
Using P2Rank
7.28
3.07
34.22
1.15
0.68
4.04
5.69
7.85
4.6 ABLATION STUDY
We conduct series of ablation studies investigate different factors affecting model performance, including the following: (1) Using a single docking module to predict both the holo structures of the ligand and pocket degrades performance for both, indicating that decomposing flexible docking into two subtasks helps reduce the complexity each module needs to handle. (2) Removing iterative update mechanism significantly impairs the ligand performance (with ligand RMSD 2A from 40.59% to 19.80%), indicating the critical role of iterative update in ligand coordinate refinement. (3) Applying the iterative update mechanism only internally within the ligand and pocket docking modules negatively impacts both ligand RMSD and pocket RMSD, underscoring the importance of prediction exchange to connect the two docking modules. (4) Replacing the pocket sites predicted by the pocket prediction module with those predicted by P2Rank reduces overall performance, sug- gesting that integrating pocket prediction within the docking process may be more effective than relying external pocket detection tool.
4.7 ANALYSIS OF ITERATIVE UPDATE
We take a case PDB 60IM as an example to visualize the structural refinements in iterative update process intuitively analyze its function in FABFlex. As shown in Fig. 4, observe

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PDB 6OIM
PDB 6ROT
Ligand RMSD
Pocket RMSD
Ligand RMSD
Pocket RMSD
Ground Truth
Ground Truth
Ground Truth
Ground Truth
FABind: 17.67
AlphaFold2:
FABind: 1.98
AlphaFold2: 2.94
FABind+: 20.15
FABind+:
DynamicBind: 18.78
DynamicBind: 1.47
DynamicBind: 1.65
DynamicBind: 1.39
FABFlex: 3.85
FABFlex:
FABFlex: 0.77
FABFlex:
Figure 5: Two case studies of PDB 60IM (left) and PDB 60RT (right).
that the ligand gradually approaches the true position of holo ligand from iteration to iteration 6. This resembles a simulation the ligand docking process, where the molecule is attracted and interacts with the binding pocket sites. This observation suggests the effectiveness and reasonability of the iterative update mechanism adopted in FABFlex. Additionally, we notice that as iterations increase, the structural changes become smaller, indicating that appropriate number of iterations is sufficient. More iterations appear unnecessary and may even increase the computational burden without significant benefit. More studies provided in Appendix C.7 for further analysis.
4.8 CASE STUDY
Fig. 5 visualizes two cases of PDB 60IM and PDB 6ROT, to demonstrate the effectiveness of FABFlex in blind flexible docking. We have following observations:
FABFlex can pinpoint the binding pocket site. In the case of PDB 60IM, existing methods such as FABind, FABind+ and DynamicBind wrongly determine the binding sites. In contrast, FABFlex successfully locates the correct binding pockets, achieving significantly lower ligand RMSD of 3.85A. This underscores FABFlex's 's ability to accurately pinpoint binding pockets, even in such difficult case where other methods fall short.
FABFlex excels in ligand structure prediction. In the case of PDB 6ROT, while all methods successfully identify the correct binding pocket, the ligand structures predicted by FABFlex are significantly closer to the ground truth compared to other methods, with ligand RMSD of merely 0. This low ligand RMSD means that the ligand predicted by FABFlex is nearly identical actual holo ligand. This visualization suggests FABFlex's 's proficiency in predicting ligand structure. Additional case studies are provided in Appendix C.11.
5 CONCLUSION
A high-efficiency molecular docking method is an effective tool in drug discovery, as it enables the fast assessment and screening of millions or even billions of potential molecule-protein interactions within a limited time, discovering promising drug candidates early in the development process. This work proposes FABFlex, a regression-based, end-to-end neural model tailored for real-world blind flexible docking scenarios. FABFlex offers solution, different from existing generative model- based sampling approaches, to achieve both fast computational speed and accurate docking per- formance. Looking ahead, we aim to explore fast and accurate multi-site docking, task of great significance and heightened complexity, as it allows for the investigation of intricate interactions across multiple binding sites.
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ACKNOWLEDGMENTS
ZZZ and BH were supported by RGC Young Collaborative Research Grant No. C2005-24Y, NSFC General Program No. 62376235, Guangdong Basic and Applied Basic Research Foundation Nos. 2022A1515011633 1632 and 2024A1515012399 MSRA StarTrack Scholars Program, HKBU Faculty Niche Research Areas No. SFENRA-IG/22-23/SCCII and HKBU CSD Departmental Incentive Scheme. JCY was supported by National Natural Science Foundation of China (No. 62306178), 111 plan (No. BP0719010).
ETHICS STATEMENT
This paper proposes a model aimed at enhancing both computational efficiency and accuracy in blind flexible docking. data used for model training and evaluation were obtained from publicly available molecular docking benchmark datasets. The advancement of molecular docking technolo- gies has both positive and potential negative implications. On the positive side, our method can contribute to advancements in drug discovery. However, there is also the risk of misuse ma- licious use, as molecular docking models could be applied to explore harmful or non-therapeutic compounds. To mitigate this risk, we releasing our code with the explicit intent of support- ing ethical and medically approved drug discovery efforts, in full compliance with relevant legal standards and institutional guidelines. This paper adheres strict research integrity protocols and responsible dataset usage, ensuring that our contributions support the broader objectives of ethical scientific progress.
7 REPRODUCIBILITY STATEMENT
The experimental settings for model are described in detail in Section 4.1. The experiments are all conducted on the widely used public molecular docking benchmark PDBBind v2020 dataset. We provide the link our source codes ensure the reproducibility of our experimental results: httpps://gt tttggggttoooooomoooorgroorrr rww.rrooroppa.lee..o
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A
METHODOLOGY DETAILS
A. DETAILS OF TRAINING LOSSES
The blind flexible docking is modelled as a supervised learning task. The ground-truth encompasses residues that form binding pocket sites, along with the actual coordinates of both holo ligand and holo pocket. The training loss implementations draw extensively from the methodologies outlined in FABind (Pei et al., 2024) and FABind+ (Gao et al., 2024). Corresponding to the statement in Section 3.2.4, we provide detailed descriptions of each training loss to improve the reprodueibility of our study:
Given that only a small fraction of the residues in a protein belong to the pocket, the pocket residue detection is treated as an imbalanced binary classification task using binary-classificatis cross en- tropy loss (BCELoss)", aiming at identifying which residues belongs to the pocket. The formulation of this loss is given by:
N
P1 My log(ú;) + (1 - リン) log(1 - Ú;)]},
Pi
ppocket.cls
(6)
N
1=1
where N is the total number of training complexes, p; the number of residues in the i-th protein, q the count residues that form the pocket in the i-th protein. The weighting factor pt/ //: adjusts the emphasis proteins with fewer pocket-forming residues, ensuring their adequate importance in the training process. Additionally, the pocket center loss, denoted as cooeer.ccnnter adopts Huber loss" to supervise the position of the predicted pocket, formulated as follows:
N N
Cpacket center
HuberLoss(center,,con center,;
(7)
where center; is the actual centroid coordinate of pocket in the i-th protein, and center; is the pre- dicted centroid coordinate, which is calculated using weighted average of all residue coordinates in the Gumbel-Softmaa dis-
the i-th protein, with the weights derived from the output prohabilities tribution. After that, the residue classification loss Clocket.oss and the pocket center loss Centert.centerr are combined to formulate the overall pocket prediction loss Lpocket.prod as follows:
Cpocket.pped = aff Cpucket + a
center peocket,centeer-
The ligand coordinate loss and the pocket coordinate loss both utilize the Huber loss regress the predicted structures towards their respective real holo structures:
Liggand coord
HuberLoss (z), 8t), Spocket coord
N HuberLoss(22) x (9)
where & (x,) and X (x.) are the predicted ligand (pocket) coordinates and actual holo ligand (pocket) coordinates respectively, for the i-th complex.
The distance map loss serves as an auxiliary objective, employing mean-squared-error loss (MSELoss)* supervise the relative positions between ligand atoms and pocket residues, formally expressed as follows:
1 Σ
Ldis_map
MSELoss(D,, D.),
(10)
where D. and D. represent the actual and predicted distance maps respectively. Each element D in D, is the Euclidean distance between the j-th atom in the ligand and the k-tth residue the pocket. Similarly, he D, corresponds to the predicted pairwise distances.
http::/wwwcctbbbooooo/////////ee http:://ww...occc........eeeeeee BCEWitLLogissLsss htt.h//ooo..mm/bbeeeiitoogtsssss
http:://yyoccooooooossssaaalaanc nn. www.://www.ooooooooo.ooeeoo
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Algorithm Pseudo code of FABFlex's inference.
1: Inputs:
2: 3D coordinates of apo ligands fx, E R
R' hisign and apo proteins fx, E R hisssm 3: Ligand atom features {h; E R Jisig and protein residue features {hj R hisism
4: Outputs:
5: Predicted coordinates of holo ligand {&, R
&R Iisign and holo pockets {&, R Reiss
6: Inference:
7: (ūj Wihisinn Ms(9, {h, XiJsiinssnn ( (siss)))), predict pocket sites
xj)
8: (1)) hsigam = (ū) @ vihissmn pick out pocket residues with Új =1
9: for iterative update k
do
G(k)x consiruct (g), {&; hisigt (x) hessam # construct ligand-pocket graph
10:
(8( hisig = (n(0( (h,, 2, hisigat, (hy, 8 hesssrr), predict ligand hSssAm = Mp(g(k) (h, x(1) hisignt (h,, x3 hessam), #redict pocket
11:
12:
(
{x)
13: end for
14: (& (xx(²) hsign', {&, Jsjsh + (x/K)) hsigar, # #final predicted structures
Return: Predicted coordinates of holo ligand (&thisgun and holo pocket {&;) ssigar.
A.2 PSEUDO CODE OF FABFLEX
To intuitively elucidate the comprehensive inference processes FABFlex, we delineate the pseudo code in Algorithm 1. The inference begins with the pocket prediction module, which identifies the binding pocket sites. Subsequently, the ligand and pocket docking modules iteratively refine and predict the holo structures of ligands and pockets.
B EXPERIMENTAL DETAILS
B.1 DETAILS OF DATASET PREPROCESSING
PDBBind v2020 widely utilized benchmark database in related molecular docking research (Pei et al., 2024; Lu et al., 2024; 2022: Corso et al., 2023). It collected 19,443 experimentally measured protein-ligand complexes along with their 3D structures. Due the absence of apo protein structures in the dataset, we follow Lu et al. (2024) and employ the well-established AlphaFold2 to predict the apo conformations of these protein structures. Consistent with above mentioned studies, we adopt the same dataset split strategy to enhance comparability. From the dataset, we select 303 test complexes recorded after 2019 and 734 validation complexes recorded before 2019, with the remaining complexes allocated for training. For the training set, complex samples that could not be processed by RDKit (Landrum et al., 2013) or TorchDrug (Zhu et al., 2022) are excluded. Further filtering excludes samples with protein amino acid chains longer than 1500 residues and molecules larger than 1 150 heavy atoms, resulting in a refined set of 12,807 training complexes. The statistics of the dataset summarized in Table 4.
Table 4: Dataset statistics of preprocessed PDBBind v2020.
avg. residues proteins
max. residues in proteins
avg. heavy atoms in ligands
max. atoms in ligands
PDBbind
# Complexes
Train
12,807
310.59
1,290
31.30
149
Validate
734
312.14
1,025
32.52
177
Test
303
280.92
1,098
35.77
147
B.2 BASELINES
We compare our proposed method with a variety of competitors, including traditional molecular docking software and recent deep learning-based methods:
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Table 5: Implementation configuration of FABFlex.
Configuration
Pretraining Stage
Pretraining Stage 2
Joint Training Stage
Learning
5c-5
5c-5
5c-6
Epoch
1000
600
Batch
Dropout
0.1
Teacher-forcing
Yes
Yes
No (p 0.5) Adam LinearLR
Optimizer
Adam
Adam LinearLR
Scheduler
LinearLR
Residue classification th
1.0
1.0
Pocket center ax
0.05
0.05
Loss weights
Ligand ducking ag
1.5
1.5
Pocket docking Oga
15.0
15.0
Distance map Ox4
1.0
1.0
1.0
Traditional molecular docking software:
Vina (Trott & Olson, 2010): AutoDock Vina is among the most widely used open-source docking programs, renowned for its enhanced docking performance. It achieves this through improvements to its scoring function, optimization algorithm and search strategy. Specifically, Vina enhances the X-score (Wang et al., 2002) and employs the Broydden-Feetche--Goldde Shanno (BFGS) (Nocedal & Wright, 1999) method for local optimization.
Glide (Friesner et al., 2004): Glide distinguishes itself from other docking software by per- forming a systematic and comprchensive search of the conformational, orientational, and po- sitional spaces the docked ligand to a rigid protein receptor. This process involves technolo- gies such as hierarchical filters, ChemScore function (Eldridge et al., 1997), among others.
Gnina (McNutt et al., 2021): Gina is advanced molecular docking software that incor- porates convolutional neural networks into its scoring function, and leverages Monte Carlo sampling comprehensively explore the conformational space of ligands.
Deep learning-based methods following protein rigidity assumption:
TankBind (Lu et al., 2022): TankBind employs the external tool P2Rank (Krivák & Hoksza, 2018) to detect potential binding pocket sites, segmenting the entire protein into functional blocks. Subsequently, TankBind uses a trigonometry-awan graph neural network to model protein-ligand interactions, predicting the distance matrix and optimizing ligand structures.
FABind (Pei et al., 2024): FABind adopts an end-to-end framework that streamlines the mod- cling of protein-ligant interactions by integrating binding pocket predictions with docking tasks. This approach accelerates the docking computation process by eliminating the need for external pocket detection tools.
FABind+ (Gao et al., 2024): FABind+ is an enhanced version of FABind, incorporating ad- ditional improvements such as dynamic pocket radius adjustments for each complex and per- mutation invariance loss to supervise ligand structures. These enhancements boost the perfor- mance predicted ligand structures.
DiffDock (Corso et al., 2023): DiffDDck utilizes diffusion models (Yang et al., 2023) to man- age the conformational refinements of ligands in molecular docking. This approach conceptu- alizes docking as a generative modeling task by mapping ligand poses onto a non-Euclidean manifold, effectively reducing the degrees of freedom for structural refinements. Additionally, DiffDock trains confidence model to estimate the poses for multiple samplings.
DiffDock-L. (Corso et al., 2024): DiffDDok--L
enhanced version of DiffDook (Corso et al., 2023) that improves docking model performance by scaling data and model size and incorporating synthetic data boost generalization capacity.
Recent deep learning-based flexible docking method:
DynamicBind (Lu al., 2024): DynamicBind is recent flexible docking method breaking down the rigid protein assumption. It leverages equivariant geometric diffusion networks to manipulate the structure changes in both ligands and proteins, recovering ligand-specific poses from their apo stages.
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20
Count
10
0.7
Vina
Glide
Gnina
TankBind
Fraction with lower Ligand RMSD
DiffDock
DiffDock-L
Dyna
FABind
0.4
FABind+ FABFlex
0.1
0.0
0
Ligand RMSD (A)
Figure 6: The cumulative distribution of ligand RMSD.
B.3 DETAILS OF IMPLEMENTATION SETTING
Model Configuration. The dimension d' of initial features extracted via TorchDrug (Zhu et al., 2022) for ligand atoms is 56, and the dimension dP of ESM-2 (Lin et al., 2022) features for amino acid is 1280. The number of FABind-layers is configured as {1, 5, 5} for pocket prediction module, ligand docking module and pocket docking module, respectively. The number of hidden size is set to (128, 512, 512} for the same modules in the corresponding order. The pocket radius of 20A is used to delineate the binding pocket sites for each ligand -potein complex.
Training details. Simultaneously optimizing all three subtasks of blind flexible docking from scratch is a challenging task. We introduce a two-stage pretraining process to warm up the model. In the first stage, the model is trained under rigid docking conditions, using a (holo protein, apo ligand) pair to predict the holo ligand structure. In the second stage, the model is trained using an (apo protein, holo ligand) pair to predict the holo pocket structure. After that, the model under- goes joint training to predict both the holo ligand and holo pocket from (apo protein, apo ligand) inputs. This progressive approach allows the model to learn from simpler tasks to more complex ones, facilitating the solution of blind flexible docking.
Training Configuration. Table summarizes the training configurations in this study. The ex- periments conducted using the Pytorch framework The model is trained eight NVIDIA RTX 4090 GPUs. The pretraining stages and reduce task difficulty by fixing either the small molecule the protein as the holo structure while keeping the other the apo structure, to warm up the model. The approximate training durations for pretraining stages and 2, as well as the joint training stage, are 100, 5, 5) days, respectively. The configurations vary slightly across different stages, mainly in terms of learning rate, and the application of teacher forcing, During the pretrain- ing stages, and teacher forcing utilized kick-start the model effectively. As the progress the joint training stage, the learning rate is reduced to better adapt the model to flexible docking.
https://ptoocch.org/
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Table 6: Analysis of the number of samplings.
Ligand RMSD (A)
Pocket RMSD (A)
Average Runtime (s)
Methods
Mean
Median
<2A(%) f
Mean
Median
DynamicBind(1 I)
6.26
3.45
27.15
0.84
0.59
22,04
DynamicBind( 10)
6.21
3.41
27.48
0.84
0.57
47.22
DynamicBind(4D)
6.19
3.16
33.00
0.84
0.58
102.12
FABFlex
5.44
2.96
40.59
1.10
0.63
0.49
Ligand RMSD (A)
Ugand RMSD =2 (%6
Helo Protein Apo Protein
Ligand RMSD 8
45.0
Procein
Apo Protein
6.0
40.0
Ligand
30.0
25.0
4.5
20.0
4.0
FABind
DifTDock-L
FABFlex
FABind
FABind+
DiffDock
FABFlex
Figure 7: Performance from holo proteins to apo proteins. Left: ligand RMSD. Right: ligand RMSD
< 2A.
C ADDITIONAL EXPERIMENTAL RESULTS
C.I DISTRIBUTION OF LIGAND PERFORMANCE
To showcase the ligand performance comprehensively. Fig. 6 provides the cumulative distibution of ligand RMSD. It can be observed that FABFlex occupies the topmost position in the distribution curve the majority of cases. Especially for the cases of ligand RMSD < 2Å, which are crucial in- dicators for evaluating molecular docking method, the superiority of FABFlex becomes even more distinct. This findings corroborates previous results Table 1, further highlighting the advantages of FABFlex in ligand performance.
C.2 ANALYSIS NUMBER OF SAMPLINGS
In this section, we specifically vary the number of samplings in {1, 10, 40} of DynamicBind for further analysis. Table illustrates the experimental results. can be observed that the number of samplings decreases, there is a notable performance degradation for DynamicBind, for example, the percentage of ligand RMSD less than 2A drops from 33.00% with samplings to 27.15% with one samplings. Besides, even DynamicBind(1) only sample once, its runtime is still much slower than that of regression-based models. These observations reflect the inherent trade-off between performance and efficiency in these diffusion-based methods like DynamicBind, where the runtime grows with the number of samplings.
C.3 PERFORMANCE FROM RIGID TO FLEXIBLE
In this section, we conduct experiments to evaluate the performance differences between rigid pro- tein docking and flexible docking. Fig. 7 illustrates comparative performances when holo and apo proteins are employed as inputs molecular docking experiments. We observe notable decrease in performance when transitioning from holo to apo structures across all rigid docking methods in- cluding FABind, FABind+ and DifiDock-L. Conversely, FABFlex consistently maintains its perfor- mance, whether measuring ligand RMSD or the percentage of ligand RMSD < 2A. These results indicate that FABFlex's 's superior ligand performance over the FABind series can be attributed to its self-adaptive capability to accommodate protein conformational changes. This capacity allows FABFlex to effectively handle molecular docking under both rigid protein assumption and flexible docking condition.
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Ligand RMSD (A)
Ligand RMSD x 2A (%)
7,5
45
Uigand PMED de
50
20-
15-
08
Figure 8: Results of adding noise. Left: ligand RMSD. Right: ligand RMSD < 2A.
Table 7: Impact of the number of iterative updates.
Ligand RMSD (Á)
Pocket RMSD (Å)
Pocket Center (A)
Average Runtime (S)
Iteration
Mean 4.
Median
<2A(())
Mean
Median
MAE L
RMSE |
FueDist
# ITER=1
6.11
3.69
21.45
1.12
0.64
3.33
4.90
6.69
0.11
# ITER=2
5.50
3.01
36.96
1.13
0.65
3.31
4.86
6.63
0.18
# ITER=4
5.11
2.91
39.27
1.14
0.64
3.31
4.88
6.64
0.33
ITER=6
40.59
0.63
3.29
4.83
0.49
ITER=8
5.33
2.87
38.94
1.10
0.63
3.28
4.79
6.58
0.60
C.4 ROBUSTNESS EVALUATION
To further evaluate the robustness and corroborate the discussion in Appendix C.3, we conduct the experiments that introduce perturbations into the input protein conformations. Specifically, we in- crementally introduce standard Gaussian noise (mean = 0, standard deviation = 1) to the coordinates within the binding pocket. The noise added based on a probability parameter Proise which varies from 0.1 to 1.0 with interval 0. that is, meaning that each coordinate has a probability Pacise of having noise added. To enhance the reliability of the results, each experiment is repeated with three different random seeds, and the mean results along with their standard deviations are reported. The results are illustrated Fig. As expected, the performance of methods inevitably deteriorates as the noise increases, observed in the figure. However, the curve corresponding to FABFlex consistently demonstrates the better performance. This suggests that, despite the lack of specific robustness- focusee design in FABFlex, it still exhibits a certain degree of robustness, capable of handling a certain level perturbation the input protein structures.
C.5 HYPERPARAMETER ANALYSIS
We conduct experiments analyze the impact of varying the number of iterative updates. Table summarizes the results. We observe that as the iterations increase, the ligand RMSD metrics improve and tend stabilize, and the average inference times also increase. In contrast, the improvement in the metrics of pocket RMSD is minimal, and the metrics for pocket center show a slight improve- ment. These findings underscore the impact of the number of iterative updates refineing ligand structures, as an appropriate number of iterations can greatly enhance the structures of ligands.
Table 8: Assessment PoseBuster test suites.
Methods
Ligand RMSD 2Â (%)
PB-valid & Ligand RMSD < (%)
FABind FABind+
22.52
1.32
34,11
8.94
DifiDock-L
37.42
16.89
DynamicBind
30.79
14.57
FABFlex
13.91
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Valddty Hate PUBSiid Test Set by PoseBuuster Suite
180-
Aggidd
PABind-
Dynammttind FABFlox
Waldky
not Luaded
atann commected
dessai foret, cherecchemisst
metlenstle Ronts
intratedinal chirality
Isrood fengeite
bend orgpec
Intemaal stork clasts
aromatic riig fitreet
desble tond farneni
proteto ligane, maxiinurt, distarce
internal werrgy
minimum distancee organicc cofectoree
minimure distonce 1o, proteie
maiiimum Inurgeni, cofectire
mantinmm distance entnr
votumme ovee sullt irpusiic colactor
vaturng everiag protein
salfomee evertap with inorgantic cotdctoro volumn cveriag with ststerd
Iath converte
modecciaa
Figure 9: Assessment of 20 aspects by PoseBuster test suites.
C.6 ASSESSMENT OF POSEBUSTER TEST SUITES
To assess the physical validity of generated ligand structures, we use the PoseBuster (Buttenschoen al.. 2024) test suites performing on the flexible PDBBind evaluate the ligand structures pre- dicted by various docking models. Table 8 summarizes the experimental results. Following Pose- Buster, we call that molecule poses which pass all tests in PoseBusters are "PB-valid". It can be observed that FABFlex achieves the highest RMSD < 2Å percentage of 39.40% among all competi- tors, demonstrating its superior accuracy in predicting docking poses close to the ground truth holo ligands. However, the percentage of "PB-valid & RMSD < 2Å" of FABind, FABind+, and FABFlex lower than those of DifiDock-LL and DynamicBind, reflecting the strength of diffusion based mod- els in generating physically valid molecular poses. This may be attributed to that diffusion- models adjust molecular structures through translational, rotational, and torsional movements, re- ducing the degrees of freedom. Notably, FABFlex achieves "PB-valid & RMSD < 2'" (13.91%) comparable to DynamicBind (14.57%), larger than poor FABind (1.32%) and FABind+ (8.94%), showcasing its balance in pursuing both accuracy and physical validity. Moreover, to analyze the physical validity comprehensively, we showcase the validity rates of all aspects by PoseBuster test suites in Figure 9. It can be observed that core limitation regarding the physical validity of deep learning- based docking models lies in the "minimum distance to protein", that is, the distance be- tween protein-ligane
atom pairs is larger than 0.75 times the sum of the pairs van der Waals radii. All docking methods demonstrate a low validity rate in this aspect. Additionally, the validity of other critical factors, such as setrrahedral.hhirlite vyolume_overlay titthhhtte-ppoeenn also requires further improvement.
"internalsterriccllast"" "internal_enerry"" and
C.7 ADDITIONAL CASES FOR ANALYSIS OF ITERATIVE UPDATE
Fig. 10 provides additional three cases to further support the discussion in Section 4.7. case PDB 6PGO and PDB 6GZY, we observe a similar iterative update process seen in PDB 60IM. The ligands gradually approach the true holo structures from iteration to iteration 6. The case of PDB 6CJJ is slightly different. We observe that the ligand is already positioned close to the holo ligand at iteration 1. From iterations to 6, the ligand undergoes continuous refinement, and by iteration 6, aligns very closely with the true holo structure. Consequently, these cases suggest that the iterative update process not only rectifies ligand positions when far from the holo ligands, but also refines the ligand conformations that are already near the correct binding position.
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PDB 6PGO
Iteration
Iteration
Ireratiaa
Iteraiin
Beeaaiio
lteration 6
POB 6GZY
Iteration
Iteration
Ireratica
Ireratina
Berntion
Herrtton
PDB
Derstion
Iteration
Iteratiea
Iteatto 5
Bernttonn
Figure 10: Three more case studies of PDB 6PGO, PDB 6GZY, and PDB 6CJJ, intuitively show- case the iterative update from iteration to iteration 6.
Table 9: Analysis of clash score.
Methods
Vina
Glide
Gnina
TankBind
FABind
FABind+
DiffDock
DynamieBind
FABFlex
clash score
0.02
0.05
0.41
0.51
0.45
0.33
0.27
0.37
C.8 ANALYSIS STERIC CLASH
In this section, conduct experiments analyze the steric clash in the predicted structures by the docking methods. Following DynamicBind (Lu et al., 2024), the clash score is defined as the voot-mean-square of the van der Waals overlaps for all atom pairs between the ligand and the protein where the interatomic distance is less than 4A, formulated as follows:
VdW overlop?
clash score
i==
(11)
N
where N is the number of atom pairs with distances considered. A lower clash score indicates fewer or less steric clashes. Table 9 illustrates the class scores across various competitors. It can be observed that traditional docking software achieves significantly lower clash scores, whereas all deep learning- based docking methods exhibit higher clash scores. This reflects core limitation of deep learning-bant docking methods: despite their improved docking accuracy, they are more prone to steric clash issues. Addressing this challenge is promising direction for future research.
Table 10: Analysis of binding affinity, including IC50 and K value.
Methods
FABind
FABind+
DynamicBind
FABFlex
IC50 (joM)
6.32
6.24
6.27
5.92
K Value (nM)
6.23
6.19
6.18
6.10
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C.9 ANALYSIS OF BINDING AFFINITY
In this section, we conduct experiments to evaluate the binding affinity of predicted structures of ligand-protein complexes. Specifically, we employ the MBP (Yan al., 2024), a structure-based binding affinity prediction model, to evaluate IC50 and K value of the predicted structures as indica- tors of binding affinity. IC50 represents the concentration of ligand required to inhibit 50% of the protein's 's activity, while K Value reflects the dissociation constant, directly measuring the strength of the gggand-protein binding affinity". For both metrics, smaller values indicate stronger binding afñin ity and better docking quality. Table 10 summarizes the experimental results. It can be observed that FABFlex achieves the lowest IC50 at 5.92 μ& and K Value at 6. 10 μM among all competitors. This observation suggests that FABFlex has potential to better predict biologically relevant interactions.
Table 11: Performance comparison in pocket- flexible docking.
Ligund RMSD
Pocket RMSD
Avg. Runtime (s)
Methods
Mean (A) +
Median 4
< 2A (%)
< 5A (%)
Mean (A)
Median (A) L
FABind
4.47
3,23
23.76
67.66
0.10
FABind+
4.21
2.66
36.63
69.97
0.14
DiffDeck-Pocket(00)
2.60
41.00
SotTDack-Pocktttae
2.60
41.70
61
ReDock(10)
2.50
39.00
74.80
ReDock(44)
2.40
42.90
76.40
FABFlex
3.45
2.57
42.24
75.25
0.93
0.66
0.47
C.10 PERFORMANCE ON POCKET-BASED FLEXIBLE DOCKING
In this section, we conduct experiments to assess the applicability of FABFlex in pocket-based flex- ible docking, where reliable prior knowledge of the binding pocket sites or sidechains is available. Specifically, the pocket prediction module is bypassed, and the partial teacher-forcing strategy is removed to adapt FABFlex model to pocket-based docking, where we feed the ground truth pocket amino acids and try to predict the holo structures of ligand and pocket. Moreover, ally evaluate FABind and FABind+ in pocket-based docking scenario, and we include the results reported in DiffDock-Pocker (Plainer al., 2023) and ReDock (Huang al.) for comparison. The experimental results are summarized in Table 11. can be observed that FABFlex also effec- tive in pocket-based docking scenario, achieving a Ligand RMSD 2Å of 42.24%, outperform- ing FABind, FABind+, DiftDock-Pocke((10) 10), DiffDock-Pocke((0)) and ReDock(10) and compa- rable to ReDock(40) of 42.90% Furthermore, FABFlex achieves a Pocket RMSD Mean of 0.93 A, demonstrating its ability to model protein flexibility. On Ligand RMSD < 5 FABlex achieves 75.25%, comparable to ReDock(40), surpassing FABind, FABind+ and ReDock(10). Notably, the average runtime of FABFlex is only 0.47 seconds, which is considerably faster than DiffDock- Pocket(40) and ReDock(40) (more than 100x times faster), indicating the strong efficiency of our FABFlcx. These results suggest that the regression-basee paradigm has the potential and capacity to handle protein flexibility in both blind and pocket-basee based docking scenarios.
addition-
1.I1 ADDITIONAL. CASE STUDIES
In this section, provide six additional cases to further support the discussion in Section 4.8. Fig. 11 visualizes four cases of PDB 6PGO, PDB 6PGP, PDB 6EEB, and PDB 605G. FABFlex suc- cessfully positions the ligands in the correct pocket regions and achieves the lowest ligand RMSID. Fig. 12 visualizes two cases of PDB 6CYH and PDB 6N8X, in which FABFlex closely approximates the ground-truth ligand structures, with ligand RMSD of 0.94A and 0.73 respectively. Further- more, FABFlex positively reduces pocket RMSD across all cases. These cases highlight FABFlex's proficiency in accurately identifying pocket sites and predicting ligand structures.
C. CASE STUDIES OF POCKET PREDICTION
In this section, Fig. 13 visualizes two cases of PDB 6QQW and PDB 6HHJ, to intuitively showcase the effectiveness of our FABFlex in binding pocket prediction. It can be observed that for both two
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PDB 6PGO
PDB 6PGP
Ligand RMSD
Pocket RMSD
Ligand RMSD
Pocket RMSD
Ground Truth
Ground Truth
Ground Truth
Ground Truth
FABind: 15.66
AlphaFold2: 1.73
FABind: 16.66
AlphaFold2: 1.80
FABind+: 4.34
FABind+: 18.29
DynamicBind: 16.69
DynamicBind: 1.52
DynamicBind: 17.56
DynamicBind: 1.23
FABFlex: 2.99
FABFlex: 1.70
FABFlex: 4.25
FABFlex: 1.76
PDB GEEB
PDB 605G
Ligand RMSD
Pocket RMSD
Ligand RMSD
Pocket RMSD
Ground Truth
Ground Truth
Ground Truth
Ground Truth
FABind: 15.18
AlphaFold2: 3.38
FABind: 11.95
AlphaFold2: 3.36
1ABind+115.59
FABind+: 15.52
DynamicBind: 29.28
DynamicBind: 3.34
DynamicBind: 29.12
DynamicBind: 3.34
FABFlex:
FABFlex: 3.37
FABFlex:
FABFlex: 3.35
Figure 11: Four more case studies of PDB 6PGO, PDB 6PGP, PDB 6EEB, and PDB 605G.
cases, the predicted pockets are correctly located in the correct regions, centered around the binding sites of their respective holo ligands. This ensures that the predicted ligands are also correctly positioned within the pocket region.
D BROADER IMPACTS AND LIMITATIONS
Broader impacts. In the current landscape, with the vast number known proteins and small molecules, high-efficiency molecular docking method is essential for analyzing and processing large-scale polecule-protein interactions within a limited timeframe, aiding the discovery of po- tential drug candidates with therapeutic value. Therefore, the exploration and development of fast and accurate docking methods in real-world scenarios are crucial to advancing Al-driven technolo- gies in drug discovery.
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PDB 6CYH
PDB 6N8X
Ligand RMSD
Pocket RMSD
Ligand RMSD
Pocket RMSD
Ground Truth
Ground Truth
Ground Truth
Ground Truth
FABind: 2.70
AlphaFold2: 6.81
FABind: 1.90
AlphaFold2: 7.78
FABind+: 1.55
FABind+: 1.44
DynamicBind: 1.79
DynamicBind: 1.69
DynamicBind: 2.78
DynamicBind: 1.90
FABFlex: 0.94
FABFlex: 5.13
FABFlex: 0.73
FABFlex: 6.32
Figure 12: Two more studies of PDB 6CYH and PDB 6N8X.
PDB 6HHJ
Predicted Pocket
Predicted Ugard
Holo Ligand (Ground Truth)
PDR 6OOW
Figure 13: Two case studies of PDB 6QQW and PDB 6HHJ to showease pocket prediction.
Limitations. There still are several limitations in this study. Firstly, the performance of pocket structures does not surpass that of the advanced method DynamicBind. This discrepancy motivates strategy enhance the results. Additionally,
incorporate advantages of the sampling-base deep learning-based methods tend to produce structures with more clashes compared to traditional docking software. To address this, a post-optimization method could be implemented mitigate such issues (Stärk et al., 2022). Alternatively. integrating biological or chemical constraints into model design and optimization might also help alleviate these problems (Jiang al., 2024).
25


Reviews:
[{"review_id": "BiGLexeEX1", "reviewer": "Reviewer_raSe", "summary": "This paper addresses the challenging and realistic scenario of blind flexible molecular docking, where the binding sites are unknown and the proteins exhibit dynamic behavior during the docking process. This is a critical problem that reflects how molecules interact with proteins. The paper argues that current flexible docking methods, predominantly reliant on sampling strategies with diffusion models, suffer from significant inefficiencies. To overcome this drawback, the paper explores the potential of regression-based model in handling flexible docking. Utilizing AlphaFold2-predicted apo protein conformations, this paper proposes an end-to-end regression-based model named FABFlex, designed to achieve both fast computation and accurate docking performance in blind flexible docking scenarios. Experiments show that FABFlex not only significantly enhances the ligand structures and positively performs impacts on pocket conformations, but also substantially accelerates docking speed compared to the recent SOTA method, DynamicBind.", "questions": "1.\tDoes FABFlex’s docking result have atomic clash problem? If so, how to mitigate or resolve this problem?\n2.\tCan you provide more details of the construction and workflow of FABFlex?\n3.\tCan you use some way to intuitively demonstrate the pocket prediction performance?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper tackles the blind flexible molecular docking scenario, which is a more practical and crucial setting compared to many existing studies that focus on rigid docking, where proteins are assumed to be static during the docking process.\n2.\tThe architecture of the proposed model is intuitive and easy to comprehend, with each module specifically designed to address a subtask of the blind flexible docking problem. It is easy to follow.\n3.\tThe model significantly outperforms the SOTA docking methods, such as DynamicBind, DiffDock, and TankBind, on ligand structure predictions. Additionally, it operates much faster than recent SOTA flexible docking method, DynamicBind (approximately 208 times).\n4.\tThis model maintains a robust generalization ability on those unseen proteins. The visualization of the iterative mechanism is very interesting, illustrating how the ligand is gradually docked from the apo to the holo state.", "weaknesses": "1.\tIt is unclear why the number of ligand-protein sample pairs of PDBBind v2020 used in this paper is smaller than that in the existing studies such as TankBind, FABind.\n2.\tIt seems that FABFlex relies on FABind layer as the fundamental component to construct the model, but the details of how this layer is adapted for use in FABFlex are not clearly articulated.\n3.\tIt seems that the model assumes that there is only a single binding pocket in a given ligand-protein pair, whereas in reality, there are possibly multiple potential binding pockets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rda0iI92CT", "reviewer": "Reviewer_H5gN", "summary": "In this paper, the authors introduce FABFlex, a regression-based flexible blind docking model. FABFlex uses the EGNN architecture, with FABind as its main layer. The model is composed of three modules: (1) the pocket prediction module, (2) the ligand docking module, and (3) the pocket docking module. FABFlex focuses on finding holo structures to enable flexible docking. Compared to other models, FABFlex demonstrates accuracy with a higher percentage of ligands achieving RMSD < 2 Å and RMSD < 5 Å, and it is also accurate in identifying holo pockets based on RMSD metrics.", "questions": "- Do you account for protein preprocessing during inference? Based on my experience with FABind, preprocessing typically takes around 10-15 seconds.\n- Does the flexibility in your model prevent steric clashes between protein and ligand?\n- Did you use the same timesplitting approach for training other DL-based methods as you did for FABFlex?\n- Did you experiment with different pocket site radii, such as 10 Å or 5 Å, in addition to 20 Å?\n- Are the alpha values learnable, or do you use a predetermined approach for them?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper presents all details clearly and comprehensibly.\n- This is the one of first applications of regression-based flexible blind docking.\n- It employs an interesting multi-task approach, addressing more than a single task simultaneously.\n- The use of flexibility, rather than just rigidity, in docking, combined with a regression approach, makes the paper compelling.\n- The pipeline provides a systematic, data-driven approach to molecular docking.\n- The experiments are detailed and clearly presented, with comprehensive benchmarking against other models.\n- The code is openly shared (though the README is empty; see weaknesses).\n\n**Originality:** FABFlex is innovative as a regression-based model for flexible docking. It also stands out for its multi-task capability, predicting not only apo structures but also holo-structures.\n\n**Quality:** The paper's quality is highlighted by its comprehensive benchmarking against other models. Additionally, it goes beyond ligand RMSD to include pocket RMSD analyses, further enhancing the study’s depth.\n\n**Clarity:** The paper is well-written grammatically, with equations that are scientifically clear, readable, and easy to follow.", "weaknesses": "- This study closely resembles the FABind[1,2] approach in both training and inference, with many of the techniques used already present in FABind. Consequently, flexible docking appears somewhat overshadowed by FABind.\n- It is not specified whether protein preprocessing is used for inference runtime, affecting the runtime comparison.\n- Using binary classification to identify pocket regions could limit flexibility at the atomic level; a more adaptable approach may be beneficial.\n- The study relies solely on the RMSD metric, which does not always ensure plausible structures. Metrics such as semi-empirical binding affinity, protein-ligand steric clashes, and ligand strain energy, as suggested by [3,4,5,6,7], should be considered.\n- Although proteins and ligands are visually classified as apo or holo, this classification does not guarantee bioactive, chemical, or physical plausibility. Including metrics like binding affinity, steric clashes, and ligand strain energy would provide greater insight, as demonstrated in studies like Posebuster[3], PoseCheck[4], PoseBench[5], CompassDock[6], and PLINDER[7].\n- The binding affinity, protein-ligand steric clashes, and ligand strain energy for the processed PDBBind’s apo and holo structures were not examined.\n- While RMSD may increase due to holo pocket region flexibility, the focus should instead be on binding affinity, steric clashes, and ligand strain energy, but the study only emphasizes RMSD.\n- It is unclear if other DL-based models in the benchmark used the same timesplit or were retrained for comparability.\n- If RMSD is the chosen metric, timesplit information leakage may occur, as described in the PLINDER study. Clarifying timesplitting methods would strengthen the study.\n- As a FABind user, I found the reported runtime of 0.12 sec inaccurate. When protein preprocessing is included, runtime is closer to 10-15 seconds, with 0.12 sec applying only to ligand conformation prediction. Based on FABind, I expect FABFlex’s runtime to exceed the stated 0.49 sec with protein preprocessing included.\n- Although FABFlex claims faster runtime than DiffDock, normalizing per-ligand runtime suggests DiffDock may be more efficient, as DiffDock samples ~40 ligand conformations in ~2 sec per conformation, while FABFlex takes ~10-15 sec for one prediction.\n\n**Quality:** The RMSD metric may lack biological, chemical, or physical relevance. For benchmarking, it would be better to include up-to-date metrics like semi-empirical binding affinity, protein-ligand steric clashes, and ligand strain energy.\n\n**Clarity:** The criteria for selecting the values of alphas in the loss function are not clearly explained.\n\n**Reproducibility:** Although the code is open source, the README section is empty, and usage instructions are not provided. If the README were clarified, I could test and reassess the code’s reproducibility. Additionally, the conda environment in the YAML file is named \"FABind,\" raising anonymity concerns regarding double-blind review, as it suggests potential overlap with FABind’s authors.\n\n**Minor Mistake**\n\n\"Subsequent\" is used twice:\n> \"The predicted pocket sites by pocket prediction module enable the subsequent subsequent ligand\"\n\n### **References**\n[1] Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan. FABind: Fast and Accurate Protein-Ligand Binding. arXiv preprint arXiv:2310.06763, 2023.\n\n[2] Kaiyuan Gao, Qizhi Pei, Jinhua Zhu, Kun He, Lijun Wu. FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation. arXiv preprint arXiv:2403.20261, 2024.\n\n[3] Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane.  Posebusters:  Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 15(9):3130–3139, 2024. \n\n[4] Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell. Benchmarking generated poses: How rational is structure-based drug design with generative models? arXiv preprint arXiv:2308.07413, 2023.\n\n[5] Alex Morehead, Nabin Giri, Jian Liu, Jianlin Cheng. Deep Learning for Protein-Ligand Docking: Are We There Yet? arXiv preprint arXiv:2405.14108, 2024.\n\n[6] Ahmet Sarigun, Vedran Franke, Bora Uyar, Altuna Akalin. CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning. arXiv:2406.06841, 2024.\n\n[7] Janani Durairaj et al. PLINDER: The protein-ligand interactions dataset and evaluation resource. bioRxiv, 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hi7q0UpsS9", "reviewer": "Reviewer_w792", "summary": "This paper introduces FABFlex, a framework for fast and accurate blind flexible docking. It consists a pocket identification module (blind), a ligand conformation prediction module (docking), and a protein flexibility modeling module (flexible).  FABFlex achieves sota results on  blind flexible docking benchmark, and is 208x faster than previous sampling-based deep learning methods.", "questions": "1. Why does this paper focus solely on blind (global) flexible docking, instead of exploring pocket-based (local) flexible docking? It seems feasible to adapt the framework to a local flexible docking setting and compare it against models such as DiffDock-Pocket[1], ReDock[2], FlexPose[3], and DiffBindFR[4]. \n\n[1] Plainer, Michael, et al. \"DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility.\" NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development.\n\n[2] Huang, Yufei, et al. \"Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge.\" Forty-first International Conference on Machine Learning.\n\n[3] Dong, Tiejun, et al. \"Equivariant flexible modeling of the protein–ligand binding pose with geometric deep learning.\" Journal of Chemical Theory and Computation 19.22 (2023): 8446-8459.\n\n[4] Zhu, Jintao, et al. \"DiffBindFR: an SE (3) equivariant network for flexible protein–ligand docking.\" Chemical Science 15.21 (2024): 7926-7942.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written, with clearly designed figures and thorough explanations of each component.\n\n2. The experiments, ablation studies, and visualizations are comprehensive and well-detailed.\n\n3.  The framework demonstrates strong performance and significantly faster inference times compared to sampling-based approaches.", "weaknesses": "1. The primary concern is that this work appears to be a direct application of the FABind series. It just introduces an additional pocket conformation prediction module to handle the flexible docking setting, which limits the overall contribution and novelty of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AaaoPlFodx", "reviewer": "Reviewer_R36f", "summary": "This work proposes FABFlex, a regression-based model for fast and accurate docking of protein-ligand pairs. The pipeline of blind flexible docking is decomposed of three stages: 1) predicting the binding pockets, 2) predicting the holo structure of ligand and 3) predicting the holo structure of receptor pockets. An iterative update mechanism is utilized for continuous structure refinement. Solid experimental results verify the superiority of FABFlex in terms of ligand prediction, pocket prediction and inference efficiency.", "questions": "1. In Table 2 we see that FABind and FABind+ achieve best results on some metrics, which seems somewhat counterintuitive since they are tailored based on the assumption of protein rigidity. Could you please give futher explanations of this phenomenon?\n2. In Table 3, the ablation of \"iterative internally\" shows better results on mean and median of ligand RMSD. I wonder if there is room for further improvement in protein-ligand interaction modeling.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. At a time when diffusion is prevalent, the work verifies the efficacy of iterative update mechanism for structure refinement, which achieves extremely high efficiency and better experimental results.\n2. Very solid experimental results are reported to show the superiority of FABFlex, including multiple tasks of interest in blind docking and various strong baselines.", "weaknesses": "1. Although good experimental results were achieved, the innovation of this work is relatively insufficient (e.g., using existing network architectures and training objectives), which is only reflected in the proposed iterative update mechanism.\n2. In some tasks, the results of FABFlex are worse than those of some models based on protein rigidity prior (e.g., pocket prediction performance in Table 2). This anomaly requires further explanation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)