3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
Authors: Chang Wu
Zhiyuan Liu
Wen Shu
Liang Wang
Yanchen Luo
Wenqiang Lei
Yatao Bian
Junfeng Fang
Xiang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Self-Supervised Learning
3D Molecule Pretraining
Molecular Property Prediction
3D Masked Graph Modeling
3D Auto-Encoder
Primary area: machine_learning_for_sciences

Abstract: Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL). However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its  Selective Re-mask Decoding (SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. 
  This SRD is synergistically integrated with a 3D Relational-Transformer (3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https://github.com/WuChang0124/3D-GSRD.

3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
Chang Wu"", Zhiyuan Liu"", Wen Shu', Liang Wang', Yanchen Luo', Wengiang Lei", Yatao Bian', Junfeng Fang"t, Xiang Wang
University of Science and Technology China National University of Singapore Sichuan University Institute Automation, Chinese Academy of Sciences
vuchang01240mail. ustc. edu. en, acharkg0gmail, com. shuwen@stu. scu. edu liang. wang@cripac. ia. ac. en, luoyanchenemail. uste. . edu en, wengianglei weceee.uuuuec edu. ybi anenus edu. fangjf (enngj119978mmaill com, x xiangwang
com
Equal contribution.
Corresponding author.
Abstract
Masked graph modeling (MGM) a promising approach for molecular representa- tion learning (MRL). However, extending the success of re-mask decoding from 2D 3D MGM non-trivial, primarily due two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for re- constructing re-masked atoms. To address these challenges, we propose 3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation 3D-GSRD lies its Selective Re-mask Decoding (SRD), which re-masks only 3D-relevant information from encoder representations while pre- serving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Traune Transformer 3D-ReTrans encoder alongside structure-ings pruuttre-inndpendeen decoder. We analyze that SRD, combined with the structre-indepeedeen decoder, enhances the encoder's role in MRL.. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-ther offttht-aatttt on out 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at htttts::/ggtt thu.. com/ comm/huchangg114/30-o000
Introduction
Molecular representation learning (MRL) 1+3 is fundamental to wide range downstream tasks, including de novo drug design 4. molecular dynamics simulation [5], and molecular property prediction [6] [7]. Given the abundance of unlabeled molecular data in this field, self-supervised key strategy for learning effective molecular representations. Previous
pretraining has emerged works have primarily focused on 1D molecular strings [ 9 and 2D molecular graphs [10 achieving promising results. However, they often neglect eritical 3D structural information, which is crucial for capturing molecular properties such as the highest occupied molecular orbital, molecular dynamics, and energy functions [13. This limitation has led to a growing interest in incorporating 3D molecular coordinates into pretraining frameworks.
Masked graph modeling (MGM) has emerged leading paradigm for 3D molecular pretraining, aiming to learn data distributions by reconstructing randomly masked graph features [14] 15). As illustrated in Figure [I] its 3D variant typically consists of three key components: (1) 3D graph masking, which perturbs the original 3D molecular graph by randomly masking features such as 3D coordinates, atom types, and chemical bonds 44 [6] (2) 3D graph encoder, which processes the
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Frezen Encoder, Transformer Decader
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Figure 2: Reconstruction loss across two pre- training settings. We compare settings using frozen encoders ReDTRs)) and structure- independent (Transformer) versus structure- dependent (3D-ReTrans) decoders.
Figure I: Illustaaiion of 3D MGM with re- re-mask decoding and 2D structure leakage.
masked graph to generate molecular representations; and (3) a 3D graph decoder, which reconstructs the masked features from the encoded representations. After pretraining via reconstruction, the 3D graph encoder is finetuned on downstream tasks to enhance performance.
However, long-standing challenge in MGM, mirroring similar issues in masked image modeling [17. is the misalignment between the reconstruction objective and representation learning [88] Specifically, minimizing the reconstruction error often leads models to focus on low-level graph features, such as atom types and 3D coordinates, rather than learning high-level graph semantics required for property prediction [19]. To mitigate this, re-mask decoding is introduced for MGM pretraining on 2D molecular graphs [15] 20, [8]. This method re-masks the encoder's representation of previously masked atoms before feeding them to the decoder (Figure [ In this way, the encoder prevented from reconstructing the masked atoms directly and is encouraged to focus on generating high-quality representations of the unmasked graph regions, which the decoder then uses for recon- struction. This approach shifts the encoder's focus from graph reconstruction to MRL and yields substantial downstream improvements for 2D MGM.
To adapt re-mask decoding for 3D MGM, we identify two scemingly contradictory challenges:
Leaking 2D structure decoder weakens encoder's MRL capability. The decoder should rely solely on the encoder representations to reconstruct masked features. Exposing the decoder directly 2D molecular structures (e.g., chemical bond connections) diminishes the encoder's role in MRL, as the decoder can recover masked features using the provided 2D molecular structures, even with poor encoder representations. For example, Figure 2 shows that frozen sundomly-intiialin encoder with a trainable structure-dependent decoder can achieve relatively low reconstruction loss when predicting masked atomic coordinates. Consequently, the encoder focuses less on capturing structures, leading to suboptimal MRL performance. However, existing re-mask decoding methods
15 18 20 typically use structure structure-dependent decoders like graph neural networks (21, which
exacerbate this issue.
Strueture-independent decoding can prevent structure leakage, but hinders reconstruction of re-masked atoms. A naive solution to prevent structure leakage is to use a pruuctre-independee decoder, which consumes no 2D structure input. However, this approach fails account for the relative positions and contextual relationships of re-masked atoms within the 2D graph, making it challenging to distinguish between re-masked atoms during reconstruction. To address this, we propose leveraging the 3D graph encoder to generate 2D structural contexts for re- re-masked atoms. This ensures that the encoder is effectively trained for structural representation while preventing structure leakage beyond the encoder's representations.
To address the challenges above, we introduce 3D Molecular Graph Auto-Encode with Selective Re-mask Decoding (3D-GSRD), 3D MGM framework with three key elements: (1) the Selective Re- mask Decoding (SRD) that re-masks only 3D-relevant information from the encoder representations while preserving 2D structural context; (2) a crmettre-inndppendee
decoder that derives all structural information exclusively from the encoder; and (3) 3D Relational-Transformee (termed as 3D-ReTrans) as 3D graph encoder, effectively integrating 3D molecular features (e.g., atomic coordinates) and 2D features (e.g.. bonding connections) for MRL.

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Specifically, SRD ensures the preservation of 2D graph structures during re- m--skiikig by reintroducing 2D information through a 2D graph Position Encoder (2D-PE). Crucially, this 2D-PE is trained via distillation from the 3D graph encoder's representations, ensuring its information is fully contained within the 3D encoder, as demonstrated in Section [ The distillation process also allows the 2D- PE's structural encoding capability to improve alongside the 3D encoder's s advancements during pretraining. To complement SRD, we employ a structure-indes inneeeee-nndeeedeent Transformer [22] decoder that derives 2D graph structure exclusively from the 3D encoder and the 2D-PE. Together with SRD, our decoder provides the re-masked atoms with rich 2D graph contexts that are distilled from the 3D graph encoder, while preventing 2D structure leakage.
Encoding 3D molecules is challenging due to their modti--odal nature (e.g. discrete atom types vs. continuous coordinates) and multi gaalllgg structure (e.g. atom-wist wise vs. pair-wise features). Prior works like PaiNN [33] and TorChMD-NET [2 address this by using equivariant architectures that separately process scalar features (e.g., atom types, distances) and vector features (e.g,, directional geometry). Building on these insights, we introduce 3e-ReTrrans as our 3D graph encoder, extending the Celational-Transformee [25] to incorporate both scalar and vector features while maintaining its scalability and flexibility to process both atom-wise and pair-wise features. Specifically, we introduce tailored attention mechanism that incorporates pairwise distances and interactions directly into attention weights, along with a 3D Update Layer that jointly updates scalar and vector features. This design yields strong performance on MRL and serves robust backbone for 3D MGM pretraining.
Finally, we include in-depth analysis to showcase the inner mechanism of SRD and our structure- independent decoder, demonstrating our key claims of shifting the encoder's focus to MRL while preventing structure leakage in the decoder. Based on these revealed advantages in MGM pretrain- ing, 3D-GSRI demonstrates superior performance when being fine-tuned on downstream datasets, achieving new state-of-the-art on out of molecules for MD17 (26).
2 Related Work
Molecular pretraining has emerged as a fundamental approach for molecular representation learning
27 29
critical for various downstream tasks, such as molecule property prediction.
3D Molecular Denoising and Masked Graph Modeling. Recent advances in 3D molccular pretraining have focused on 3D structure learning through coordinate denoising and masking. For example, 30-32 introduce noise to atomic coordinates and then reconstruct them. SubGDiff 2 adds distinct Gaussian noise to different substructures of 3D molecular conformation and performs denoising via a diffusion process. MolSpectra [33] uses the energy spectra to enhance 3D molecular
representation learning during denoising. As for masking, Uni-Mol [14] and Uni-Mol2 (16) employ masked coordinates prediction as one of the self-supervised tasks, while other works like focus on masking and predicting bond lengths and angles.
Other 3D Molecular Pretraining Methods. EPT 34 proposes a multi-domain 3D pretraining
approach by combining level features for small molecules and residue-leve features in proteins. 3D PGT (35 designs three generative pretraining tasks, including predicting bond length, bond angle, and dihedral angle, and introduces adaptive fusion strategy for these tasks, using total energy as a surrogate metric to optimize their combination weight. GraphMVP (36) and 3D Infomax 7 use contrastive learning to transfer knowledge from the 3D encoder into the 2D graph encoder.
Encoding and Structure Encoding. Position Encoding (PE) encodes the spatial position of a given node within a graph [37. Some methods [38 39] use adjacency, Laplacian, or distance matrices to represent PE. Other approaches like 420 leverage shortest paths, heat kernels, or Green's 's function to compute pair-wise distance, capturing the distance and directional relationships between nodes. Currently, in MOL-AE [9], SMILES strings are used to provide PE to the decoder as an identifier. Structure Encoding (SE) encodes the structural information of graphs and subgraphs. Common methods include node degree 43 Laplacian matrices [38]. and Boolean indicators that specify whether two nodes belong the same substructure
Graph Position
(44). Unlike these methods, we propose
using a 2D graph position encoder distilled from a 3D graph encoder to produce SE, which provides rich and effective context information.
2D Molecular Graph Pretraining- Previous methods primarily focus on leveraging 2D molecular graphs to learn molecular representation. A popular technique is masked graph modeling [15 8 10
3

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20 Modecular Graph (0,6)
Pusition Faceder dra dtrle
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Overview of 3D-GSRD. It contains three key elements: (1) 3D-Re encoder; (2) the SRD that re-masks only 3D-relevant information from the encoder representations while preserving its 2D structure information via 2D-from-3D distillation; (3) structure-indeppende decoder.
Figure 3:
typically comprising three key components [8] graph tokenizer [1 3 graph masking 45
46
and graph autoencoder [47 Another prominent line of work is contrastive learning [50
12 51
which aims to pull positive pairs and push negative pairs apart in the representation space. Notably, methods such as GeomGCL (6) GraphMVP (36), and 3D Infomax
[7] incorporate 3D molccular conformations auxiliary information to enhance 2D graph representations via contrastive objectives. While effective for 2D molecular pretraining, these methods overlook 3D features, which are crucial for molecular representation learning. Moreover, directly extending these methods to 3D molecular pretraining is non-trivial due to the increased complexity and spatial nature of 3D molecular data.
Our method is similar to GraphMVP [36 in leveraging both 2D and 3D molecular graphs but differs in objective and design. While GraphMVP transfers 3D information into a 2D encoder to enhance 2D graph representations, our method distills 3D representation into 2D-PE, ensuring the 2D>PE's embedding is fully contained within the 3D encoder to avoid structure leakage in decoding. Additionally, GraphMVP aligns the 2D and 3D views of the same molecule and contrasts views of different molecules using contrastive losses. Our method instead uses a cosine similarity loss that encourages 2D-PE to generate structural encodings closely aligned with the 3D encoder. This offers simpler and more efficient framework for 2D structure- structure-informed decoding without structure leakage.
3 Preliminary: 3D Masked Graph Modeling
Notations. A 3D molecular graph is represented as G = (x, a, e), where x E R-x denotes the 3D atomic coordinates, a x represents the atom types, and E NxNx* captures the atomic pair features, such as inter-atomic distances and bonds. N is the number of atoms.
Graph Masking. Given molecular graph G, the 3D coordinates {x; E I V. } of randomly selected subset of atoms Vm are masked. For each masked atom E Vm. its original coordinates x, is replaced by learnable special token m, E RR3 The coordinate matrix after masking denoted as X, and the masked graph is denoted G (x, c). Some prior works [19 instead remove all information corresponding to the masked atoms, including their 3D coordinates, atom types, and pairwise features, which results in a masked graph G (x, à, è). In this work, we adopt this latter masking strategy, fully excluding the masked atoms from the input graph.
3D Graph Auto-Encoder and 2D Structure Leakage. The 3D graph auto-encoder comprises a graph encoder (-)-) and graph decoder da(()) The encoder processes the masked graph G to produce graph representations h = de (G) E Nx. The decoder then predicts the masked coordinates {& E )m using @a(h), and optionally incorporating the pair features da (h, e). However, using pair features e introduces 2D structure leakage, as the decoder relies on additional information beyond the encoder's representation h. This weakens the encoder's 's role in MRL, because the decoder can leverage pair features to compensate for any deficiencies the encoder's representations. Despite this drawback, such leakage is common in previous MGM works [15] 20 [8] that utilize Graph Neural Networks [21] as decoders. In contrast, methods that avoid 2D structure leakage [44] [6] mostly use weak decoders, such as MLPs, which can lead to suboptimal MGM pretraining.
Re-mask Decoding. Before passing h into the decoder, re-mask decoding replaces the representations of the previously masked atoms Vm, with a learnable token my, preventing the encoder from directly

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(c) Update Layer
Figure 4: Illustration of 3D-ReTrans. (a) 3D-ReTrans is constructed by stacking multiple 3D Relational-AAtention and 3D Update Layers. (b) 3D Relational-Attentio that processes both atom- wise and pair-wise features. (c) 3D Update Layer that includes residual connection.
predicting the masked coordinates. This encourages the encoder to focus on learning meaningful representations for the unmasked graph regions. The re-masked representation h is defined as:
mp: Vi E Vm>
h; re-mask(h.))
ha: otherwise.
MGM Loss. The pretraining objective minimizes the mean squared error between the ground truth coordinates {x, Vn of the masked atoms and the decoder's predicted coordinates {& E vm
MMMM Σ 1 -x:-
x
(2)
iEV.
Methodology: 3D-GSRD
In this section, we present our method 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding (3D-GSRD) (Figure B.. Below, we start by elaborating on the Selective Re-mask Decoding and the pretraining objective 3D-GSRD. We then describe the encoder of 3D-ReTrans.
4.1 SRD: Selective Re-mask Decoding
Here we introduce SRD to improve 3D MGM. Re-mask decoding is proposed to address the mismatch between the reconstruction objective of 2D MGM and MRL [15 18 Our SRD extends this approach to 3D MGM while overcoming issues such as 2D structure leakage and providing 2D contexts to re-masked atoms.
Re-mask Decoding with 2D Graph Position Encoder. As Figure shows, given the encoder representation de (x, a, é) of G. SRD can be defined as:
SRD(h, G) re-mask(h) (h) + stop stop-grad(@za(a... e)),
(3)
where SRD(h, G) is directly fed into the decoder for masked prediction; re-mask(.) is the standard re-mask; and stop-grad(-) stops the gradient flow to the 2D graph position encoder Φ22, which generates G's 2D representation (2, e) E X=
Building 2D Graph Position Encoder without Structure Leakage via 2D-from-3D Distillation. The 2D graph position encoder (2, a, e) is the key component of SRD. For unmasked atoms, Øzd conveys the same information as the 3D graph encoder de. preventing any information leakage beyond what d. has captured. For re-masked atoms, Φ2α offers the necessary 2D contexts that would have been available from de without re-masking, enabling the decoder distinguish the relative positions of re-masked atoms. To prevent information leakage, Φ2a is trained exclusively through knowledge distillation from the 3D encoder, without any gradient updates from the MGM loss. This is enforced by the stop-grad(-), which blocks the gradient flow from the MGM loss into Φ2d. Further, the knowledge distillation loss for Φza can be written as:
Ldistill
Σ K
cos(sbala, e) stop-grad (x, a),)))
(4)
5

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where cos(-.)) denotes cosine similarity. The loss applies only to unmasked atoms for the consisteney of Oru's 's training objective. We employ stop-grad(-) to prevent updating the 3D graph encoder P.. allowing it to focus on MRI..
While the 2D graph position encoder be any graph encoder, we implement Reanne, a simplified version of the 3D-ReTraans that excludes 3D coordinates and distance inputs. Our experiments demonstrate the effectiveness of this design.
4.2 Pretraining 3D-GSRD
3D Graph Auto-Eneoder. We employ the independent decoder of transformer [22]. In this way, we avoid structure leakage to the decoder beyond the encoder's information and provide strong 2D) structural contexts for the re-masked atoms.
as encoder and employ SRD with structure-
Pretraining Loss. We combine the MGM loss and 2D-from-3DD distillation loss for pretraining: LMGM + Cdistill
Lpecrrin
4.3 3D Relational- Transfonl-
Encoding 3D molecular graphs G = (x, e) presents significant challenges of processing 3D coordinates x while preserving 3D equivariance and integrating the pairwise features e, whose shape [N, differs from the atomic features (N, +). Prior works have primarily focused on ensuring 3D equivariance while processing the 3D coordinates [24 52). but have paid less attention to effectively incorporating pair features e. Most existing methods [4
52 53 incorporate pairwise representations of scalar values in self-attention layers [22]. limiting their ability to capture the high-dimensional nature of inter-atomic interactions. While TorchMD-NET 24 models distances as high-dimensional pairwise features, extending include chemical bonds remains challenging.
To address the challenges, we propose Trans as our encoder, leveraging the Relational- Transformer's (25] scalability and flexibility to incorporate pair features, while enabling it to process 3D coordinates. Draw inspiration from prior works [24] 23 54] a core design is to explicitly separate and jointly process types of features: (1) scalar features, which encode scalar information like atom types and distances; (2) vector features, which capture directional geometric information. Based on this, our key enhancements focus on improving its attention mechanism and incorporating a 3D update layer. More details about 3D-ReTrans are provided in Appendix B
Each atom is represented by concatenating its types and coordinates: n; = |,;x;] The interaction between atoms and j is captured by the pair feature Cijy E R and their Euclidean distance rij. 3D Relational eelational-Attention is defined as:
3D Relational-Attentio
Φj [n,, Ciyl W
(6)
kyys Voy (ny: IW w"),
(7)
[dy; dij] = SiLU (Iwak; wd REF (ra)))
(8)
[s sty. s = Viy ④ (9)
dij.
aij = SiLU, O:j (ky O d
(10)
[o] [o] of 0 w/
(11)
vd
where e (R): [0, oo) + Rd is distance expansion function to encode the distance variable into d-dimensional vector 24 (see Appendix B The terms W W W Wilk Wde and W are learnable linear projectors, and denotes element-wise product. The output scalar features of of and of encode pairwise interactions and interatomic distances. Moreover, the attention mechanism facilitates the integration of distance information into the vector features via scalar filters s/ and s within the subsequent 3D Update Layer.
3D Update Layer. The 3D Update Layer facilitates information exchange between scalar and vector features. Vector features vec, E R x3 are initialized as zeros and jointly updated with scalar features I, The update Ar, and Avec, are defined as:
N
[u], , uf uf] w (Vvec):),
(12)
W
vec, @
s/y) +
r, rr - rryll
r;
(13)
Ar; = of + o (u! u&
(14)
A Avec = u o W
(15)

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Figure 6: Probe encoder for Figure 7: Reconstructing 2D-PE masked atom coordinates when representation using 3D encoder pretrained with/without SRD. representation.
Figure 5: Reconstruction loss across four pretraining settings.
where W are learnable linear projectors. Scalar features incorporate vector information via element- wise multiplication with the scalar product of vector components, while vector features are updated using both directional features w and scalar filter of.
As shown in Figure 4 the 3D-ReTrans is constructed by stacking multiple 3D Relational-AAtention and 3D Update Layers. Each layer performs residual updates on both scalar features Az; and vector features åvec, allowing the model to simultaneously capture scalar properties (e.g. atom types, distances) and expressive directional geometric information. For pretraining, the final outputs and vec, are fed into the decoder, while for finetuning, they are passed to the prediction head.
Learning 3D Equivariance and Invariance by Data Augmentations. Considering that our model lacks built-in 3D equivariance invariance, we leverage data augmentations to instill these sym- metries, following AlphaFold3 [33 During MGM pretraining, atomic coordinates x are randomly rotated using transformations sampled from the SO(3) group and translated with offsets drawn from t ~ (O, 0.0113). These augmentations encourage the model to adjust its predictions equivariantly with any rotations and small translations. During fine-tuning for property prediction, the same augmentations are applied, but the model is trained to predict consistent properties, thereby learning invariance rotations and translations.
We favor data auta-auummeted equivariance with relational attention over fully E(3)vvruiraiian message passing. While built-in equivariant architectures offer formal guarantees, they often restrict how pair features are parameterized and incur non-trivial computational overhead (e.g., tensor bases, spherical harmonics), which can hinder scaling and complicate integration with diverse molecular cues. In contrast, our approach instills rotational and translation robustness through augmentations, allowing encoder operate with lightweight vector and scalar updates and flexibly ingest high-dimensional pair features without architectural surgery. This yields a plug-and-plaa backbone that is easier to optimize, accommodates sparsity and density variations, and remains representation--ich relational attention can expand swap pair features as downstream tasks evolve, while maintaining competitive robustness to pose changes at a substantially lower training and inference cost.
5 Analyzing Selective Re-mask Decoding and Structure-Independeerr Decoder
In this section, we conduct extensive experiments to evaluate the components of 3D-GSRD, focusing on the dructure-independee decoder and SRD, including its key part 2D-from-3D distillation. We analyze their effects on the overall performance of the 3D-GSRD framework and how they contribute to 3D MGM pretraining.
Analysis The structure-dependent decoder can diminish the encoder's role in MRL. We pretrain the auto-encoder framework under four settings, all using 3D-ReTrrans as the encoder: (1) frozen encoder with Transformer decoder; (2) frozen encoder with 3D-ReTram decoder; (3) trainable encoder with a Transformer decoder; and (4) a trainable encoder with a 3D-ReTran decoder. Figure Ieports the reconstruction loss of masked atom coordinates, averaged over every 50 batches. When the encoder is frozen, the Transformer decoder (i.e., structure-indee snneture-indeeendeent decoder) struggles to reconstruct masked atoms coordinates, yielding high reconstruction loss due to poor input representations and the absence of 2D molecular structural information. In contrast, with the 3D-ReTraa decoder (i.e., structure-dependent decoder), which leverages 2D molecular structures as input, the loss decreases rapidly during pretraining, even with a frozen encoder. This demonstrates that a powerful, structure-dependent decoder can compensate for weak encoder representations, diminishing the encoder's 's role in MRL.
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The structure-independell decoder heavily relies on high-quality encoder representations. When paired with a trainable encoder, the structure -nnccurr-iddeeeddent decoder achieves a much lower reconstruction loss, indicating that it relies heavily on the encoder to provide informative representa- tions. In contrast, with a structure-dependem decoder, the loss remains low regardless of the encoder representation's s quality, highlighting that such decoders reduce the learning pressure on the encoder and can hinder its ability to learn meaningful molecular features.
Analysis 2. Structure-indepeeneett decoder improves downstream performance compared to structure-dependent decoder. We pretrain the auto-encoder using either a structure dependeet or a structure- -nntreedendd decoder while keeping all other settings constant, then finetune the pretrained encoder on molecular property prediction tasks. As [able[ssows the structure- inntturr-iddeeeddeen decoder achieves better performance, outperforming the structure-dependerr dependent decoder by 5% in Toluene. This demonstrates that using a prrueture-iddeeendent decoder encourages the encoder learn more informative representations, leading to improved performance on downstream tasks.
Analysis 2D-PE and 2D-from-3D distillation boost downstream performance. We pretrain the auto-encoder with and without 2D-PE and 2D-from-3D distillation, followed by finetuning the encoder on MD17 datasets. As shown in [able[]] combining 2D-PE and distillation consis- tently improves performance. In contrast, using 2D-PP alone leads to degradation, likely due to unintended leakage of 2D structural information into the decoder. Moreover, 2D-from-3D distil- lation guides the 2D-PE to focus on encoding positional information for re-masked tokens, rather than learning molecular representation, which allows the 3D graph encoder to better specialize in MRL.
Table I: Analyzing the decoder and SRD. Perfor- mance (MAE 4) MD17. The variant without SRD and Lassill corresponds to the model ablated without 2D-PE.
Decoder
SRD Cavi Salicylic Toluene Uracil
Simuctrreddependent Structure-ineepeddee fimcture-indepenneer Structure- independent
/
/
0.0401
0.0291
0.0334
X
x
0.0404
0.0292 0.0328
0.0416
0.0293 0.0329
V
0.0387
0.0275 0.0315
Analysis 4. SRD prevents the encoder representation from containing information about 3D coordinates. To examine whether the encoder captures detailed 3D coordinate information, we train an MLP probe to predict masked atom coordinates from the encoder's 's representations, We compare the reconstruction loss for encoders pretrained with and without SRD. As shown Figure 6 the reconstruction loss for the encoder pretrained with SRD is much higher, suggesting that SRD suppresses direct encoding of 3D coordinate details. This forces the encoder to focus on learning higher-level molecular representations that are better aligned with downstream tasks.
Analysis 5. 2D-PE produces 2D structural context without introducing information leakage. To assess whether 2D-PE introduces additional information beyond what the 3D graph encoder already captures, we train an MLP reconstruct the 2D-PE representation from the 3D graph encoder representation and compute the reconstruction error, measured by the cosine similarity between the two representations. During this process, both the 3D graph encoder and 2D-PE are frozen, and only the MIP is trainable. Figure shows that the cosine similarity is very close to 1.0, indicating that the context 2D-PE provided mostly contained by the 3D graph encoder.
Analysis 6. 2D-PE encodes structural information for decoding. We probe whether the pretrained 2D-PE captures structural information required by the decoder. Specifically, we freeze the 2D-PE and train two MLP classifiers to predict atom and bond types from its outputs. Both tasks achieve prediction accuracy above 99. 99.99, demonstrating that the 2D-PE indeed encodes the structural information necessary for decoding.
Experiments
6.1 Experimental Setup
Datasets. For pretraining, we use a large-scale molecular dataset PCQM4Mv2 (55), which contains approximately 3.37 million equilibrium 3D molecular graph structures. For downstream tasks, we evaluate our model on two widely used molecular property prediction datasets: QM9 [13 and MD17 (26]. Specifically, QM9 is quantum chemistry dataset comprising 134k small molecules, each with its equilibrium conformation and 12 molecular properties (e.g., homo, lumo, dipole moment
etc.) calculated using density functional theory (DFT). Following prior works [30 31], we split
8

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Table 2: Performance (MAE on MD17 force prediction. The best results are bold. The second-best results are underline. Results marked with are reproduced by us.
Models
Aspirin
Benzene
Ethanol
Malonaldehyde
Naphthalene
Salicylic
Toluene
Uracil
TorchMD-NET
0.1216
0.1479
0.0492
0.0695
0.0390
0.0655
0.0393
0.0484
3D-EMGP
0.1560
0.1648
0.0389
0.0737
0.0829
0.1187
0.0619
0.0773
3D-EMGP
(TorchMD-NET)
0.1124
0.1417
0,0445
0.0618
0.0352
0,0586
0.0385
0.0477
Frad*
0.0825
0.1355
0.0432
0.0535
0.0431
0.0569
0.0433
0.0482
3D-ReTrrans
0.0726
0.1619
0.0556
0.0659
0.0423
0.0523
0.0417
0.0427
3D-GSRD
0.0583
0.1435
0.0355
0.0468
0.0266
0.0356
0.0274
0.0292
Table 3: Performance (MAE 1) on QM9. The best results are bold. The second-best results are underline, Results marked with are reproduced by us.
lumo gop (meV) (meV) (meV)
homo
R2 (a))
ZPVE
G
C
Models
(D)
(ag)
(meV) (meV) (meV) (me
(me V)
col munl
Uni-Mol2
0.089 0.305
5,26
0.144
SchNet
0.033 0.235
41.0
34.0
63.0
0.07
1.70
14.00
19.00
14.00
14.00
0.033
E(n)-GNN
0.029 0.071
29.0
25.0
48.0
0.11
1.55
11.00
12.00
12.00
12.00
0.031
DimeNet++
0.030 0.043
24.6
19.5
32.6
0.33
1.21
6.32
6.28
6.53
7.56
0.023
PaiNN
0.012 0.045
27.6
20.4
45.7
0.07
1.28
5.85
5.83
5.98
7.35
0.024
SphereNet
0.025 0.045
22.8
18.9
31.1
0.27
1.12
6.26
6.36
6.33
7.78
0.022
ComENet
0.025 0.045
23.1
19.8
32.4
0.259
1.20
6.59
6.82
6.86
7.98
0.024
ToTThMD-NET
0.011
0.059
20.3
18.6
36.1
0.033
1.84
6.15
6.38
6.16
7.62
0.026
3D-ReTTrans
0.016 0.055
22.0
17.8
38.0
0.341
1.85
6.18
6.36
6.51
7.89
0.029
Transformer-M M
0.037 0.041
17.5
16.2
27.4
0.075
1.18
9.37
9.41
9.39
9.63
0.022
BB(3)-DDM
0.015 0.046
23.5
19.5
40.2
0.122
1.31
6.92
6.99
7.09
7.65
0.024
3D DD-EMGP
0,020 0.057
21.3
18.2
37.1
0,092
1.38
8.60
8.60
8.70
9.30
0.026
Coord
0,016 0.052
17.7
14.7
31.8
0.450
1.71
6.57
6.11
6.45
6.91
0.020
Frad
0,012 0.045
15.4
13.7
30.6
0.428
1.56
15.88
14.67
14.87
13.52
0.023
SliDe
0,015 0.050
18.7
16.2
28.8
0.606
1.78
10.05
10.79
11.34
11.80
0.025
Mol-AE*
0.152 0.434
6.962
0.215
Uni-GEM
0.019 0.060
20.9
16.7
34.5
0.023
3D-GSRD
0.009 0.038
18.0
14.5
31.1
0.047
1.38
5.48
5.67
6.90
0.020
the dataset into 1000/11000/10,83 molecules for training. validation, and testing, respectively. MD17 provides simulated dynamical trajectories for 8 small molecules, including their energy, forces, and conformations. During finetuning, our model first predicts the molecular energy and subsequently derives the forces using the relationship F' = ,, where r represents the 3D coordinates. For finetuning, we split the dataset into 9500/950 samples for training and validation, and use the remaining samples for testing.
Baselines. To evaluate the effectiveness of our proposed framework, adopt state-of sefftthh-rtt 3D
molecular pretraining methods and supervised models for molecular property prediction as baselines.
For 3D molecular pretraining methods, we include Transformer-M [52], SE(3)-DDM [56]. 3D-EMGP
57]. Coordinate Denoising (32] Fractional Denoising (30]. Sliced Denoising 31
UniGEM 58)
Mol-AE [T9] For supervised models, we include SchNet (59), E(n)-GNN 600 DimeNet 61
DimeNet++ (62] PaiNN 23), SphereNet (63], TorchMD-NET (4), Uni-Mol2 (66,, ComENet 64
We also include the results of training our backbone (i.e., 3D-ReTrans from scratch to evaluate the effectiveness of our pretraining methods. We reproduce the results for Frad, SliDe and Mol-AE, while the results for other baselines are directly taken from the referenced papers. More details about baselines and implementation are provided in Appendix
6.2 Results on MD17
The MD17 dataset contains diverse non non-equilibrium molecular structures that are highly sensitive to geometry, making it a challenging benchmark for 3D MRL. As shown in Table 3D-ReTrans achieves performance comparable to TorchMD-NET and surpasses 3D-EMGP on of molecules, demonstrating its strength as a 3D graph encoder for 3D MGM. Moreover, 3D-GSRD attains state-of- the-art results on of molecules except Benzene, exceeding the strongest baseline (i.e., Frad) by a large margin. These results confirm the effectiveness of our pretraining method.

---

Table 4: Ablation 3D-Re'rram components. Performance (MAE .) on QM9.
Table 5: Analyzing SRD the Relational- Transformer. Performance (MAE 4) on MD17.
Model Components
homo
lumo
xpve
Decoder
SRD
Lava
Toluene
Uracil
Relational-Transfrmmee
27.7
24.0
1.97
Structure-dependent
0.1144
0.0813
+ 31 Data Augmentation
24.6
23.2
1.92
Structre-indepeenae
0.1250
0.0828
3D SclationalAAttattion
23.4
20.3
1.90
Structur-inndeenndeer
X
0.0998
0,0843
+ 3D Update Layer (3D-ReTrans)
22.0
17.8
1.85
Structure indieeedennd
0.0745
0.0733
6.3 Results on QM9
We also evaluate the effectiveness of 3D-ReTrans and our pretraining strategy on the QM9 dataset, as shown in Table 3D-Re'rrans achieves performance comparable to TorchMD-NET,, validating the effectiveness of our proposed backbone architecture. Moreover, 3D-GSRD sets a new state-of-the-art on 7 out of 12 properties, surpassing most baselines, including methods with and without pretraining. These results demonstrate that 3D-GSRD is highly effective pretraining strategy for MRL, offering advantages over coordinate denoising based approaches.
6.4 Ablation Studies and Analysis
Ablation on Backbone. To assess the effectiveness of our improvements to the Relational- Transformer [25), we perform ablation studies on each component of 3D-ReTrans, as summarized in Table 4 The results show that 3D Relational-Attention, the 3D Update Layer, and 3D data augmentation each enhance molecular property prediction, collectively boosting overall performance.
Generalization of SRD Across 3D Graph Encoders. To evaluate the generalization SRD, we replace 3D-ReTrans with the Relational-Tramn Transformer and conduct additional experiments. As shown in Table incorporating SRD consistently improves downstream performance. demonstrating its effectiveness general pretraining strategy applicable to diverse 3D graph encoder architectures.
Ablation on 2D Graph Position Encoder. We compare our 2D-PE against alternative structural embeddings, such as those in GraphGPS (37]. Following prior results, we adopt RWSE 665 as representative baseline due to its strong performance on ZINC and PCQM4Mv2 with relatively low computational cost. As shown in Table replacing 2D-PE with RWSE leads to consistently lower performance, confirming the advantage of 2D-PE in providing 2D structural context.
Table 6: Ablation on 2D graph po- sition encoder. Performance (MAE 4) MD17.
2D Encoder Salicylic Uracil
RWSE 2D-PE
0.0368
0.0310
0.0356
0.0292
7 Conclusion and Future Works
In this work, we introduce 3D-GSRD, a 3D MGM framework with three key components: (1) the Selective Re-mask Decoding that selectively re-masks 3D-relevant information while preserving 2D graph structures; (2) a structure- -nneeuur--ddeeeedeent decoder that eliminates all structural information by relying solely encoder representation; and (3) AD-ReTrans the 3D graph encoder for MRL. Our detailed analysis reveals the internal mechanisms of SRD and the structure- inneeeee-ndeee decoder. Extensive experiments demonstrate that 3D-GSRD significantly outperforms baselines downstream datasets such as QM9 and MD17.
Despite promising results, several limitations remain. Our pretraining is conducted on PCQM4Mv2 (55) with 3.37M molecules, which is smaller than large-scale datasets such as PubChemQC (66) with 230M molecules, potentially constraining performance. Scaling to larger and more diverse datasets an important direction. addition, we focus on molecular property prediction, while other tasks like 3D molecule generation [67-70] and multi multi-modal molecule-ter modeling [71] could also benefit from our pretrained autoencoder. Beyond molecular applications, our pretraining paradigm can be extended to broader biological modalities such as single-cell [75] and protein (76).
Acknowledgement
This research is supported by the National Natural Science Foundation of China (62572449, 624B1012) and National University of Singapore SoC (grant no: A-0010308-00-00)
10

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[73] Zhiyuan Liu. Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, and Tat-Seng Chua. Reactxt: Understanding molecular "reaction-ship" via reaction- contextualized molecule-text pretraining. In Findings of the Association for Computa- tional Linguistics: ACL 2024. Association for Computational Linguistics, 2024. URL
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A Broader Impacts
This work advances molecular representation learning and has the potential accelerate downstream applications such as molecular property prediction, drug discovery. molecular dynamics simulation, and material design, helping reduce the and time of wet-lab experiments. However, there is risk of over-reliance on model predictions without sufficient interpretability or domain validation. Additionally, models trained on biased datasets may produce structural or chemical biases, limiting the model's generalization across molecular spaces. We encourage the community to test our models strictly before applying them in scientific scenarios.
B More Details on Methodology
In this section, we describe the embedding layer preceding the attention mechanism in 1D-ReTTrans, The imput 3D molecule graph is defined as G (x, a, e). where x denotes atomic coordinates, represents atom types, and denotes pairwise edge features. Our goal is to obtain atomic and edge embeddings that encode both chemical and geometric context.
The initial node embedding node jointly encodes atom coordinates and types:
mode Eoded ((x, a)).
(16)
To incorporate local geometric context, we compute the neighborhood embedding 6, atom based on the radial distances dı to neighbors atom j. The radial basis function is given by:
neigh for each
e RBF (diz) = of dig) exp (-3 (exp(-d;) - u)²)
(17)
(ddy)
2
cos
Idyy deut
+1),
if d, S deut
(18)
0,
if d,j > deut
where ((dy)) is a smooth cutoff function ensuring locality and and μ are fixed parameters. The neighborhood embedding
neigh for atom i is then defined as:
N
noigh
Σ
Emihd ([x, al)
W RBF (d:z);
(19)
where @ denotes clement-wist product and W is learnable projector. We set the cutoff distance deut = 5 A, ensuring that each atom only attends to neighbors within this spatial range.
The final atomic embedding e, allmmic combines node and neighborhood information:
stomic Wa
W[
Dode
peigh
(20)
where a denotes learnable projector.
We also obtain the edge embedding edge via:
edge Embed odg (c).
(21)
C Pseudo Code
We present the pseudocode for pretraining (see Algorithm I and finetuning (see Algorithm algorithms in this section.
2
D Experimental Setup
D.1 Computational Resource
All experiments are conducted on NVIDIA A6000-48G GPUs. Pretraining requires a total of 48 GPU hours. For downstream tasks, finetuning on QM9 and MD17 takes approximately 48 and 8 GPU hours per experiment, respectively.
23

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Algorithm Pretraining of 3D-GSRD
Require: 3D graph encoder Φg 2D graph encoder og decoder ofe pretraining dataset D, input 3D molecule graph G = (x, a, e), masked coordinates prediction head PasHead,, denoising prediction head Denoise eadg, mask ratio p, denoising loss weight w.
1: while training is not finished do
2: G. = (x, a, c) = dataloader(D))
3: randomly mask atoms and add Gaussian noise (Ar; ~ 0.04-NV(0, o² Im)) to the unmasked
atomic coordinates
4:
input moleule G, (ā
5:
haD vec 2
6:
h2D 00 (a)
7:
SRD(haD, G,) re-mask(hsp) hop-grad(h2p)
8:
rep, vec 20 (SRD(haD G.), vec)
For masked atoms: afred PosH eads (rep, vec)
9:
10: For unmasked atoms: Axpred DenoiseH eads (rep, vec)
11: Loss
+w- ||r Ar. (osiieSSiillarittystooooasss grad(hap)) hap)
Optimise(Looss)
13: end while
Algorithm 2 Finetuning of 3D-GSRD
Require: 3D graph encoder d3 finetuning dataset D, input 3D molecule graph G = (x, a, a),
label prediction head LabelHeadg.
1: while training is not finished do
2: G2 (x, a, c). 4/2 dataloader(D)
3: hap: vec 08 (G:)
4: yr y. = Label (haD, vec)
5: Loss Pred yill2
6:
Optimise(Loss)
7:
end while
D.2 Baselines
We describe the details of our reported baseline methods in this section.
SchNet [59] proposes continuous-files convolutional layers, which enables the model to capture local correlations in molecules without grid-based data.
E(n)-GNN 600 introduces an architecture that is equivariant to rotations, translations, reflections, and permutations. Notably, its equivariance extends higher dimensions with affordable computational overhead increase.
DimeNet [61] applies directional message passing which enables graph neural networks to incorporate directional information for molecular predictions and using spherical Bessel functions and spherical harmonics to representation distances and angles.
improves upon DimeNet by being 8x faster and achieving 10% higher accuracy, while maintaining strong generalization across molecular configurations and compositions.
DimeNet++ 62
PaiNN 23 addresses the limitations invariant representations in message passing neural networks by extending the message passing framework to rotationally equivariant representations.
analyzes 3D molecular graphs in the spherical coordinate system and propose the spherical message passing (SMP) scheme to efficiently distinguish molecular structures while reducing training complexity.
SphereNet 63
TorchMD-NET introduces an equivariant Transformer architecture with a modified attention mechanism that incorporates interatomic distances directly into the attention weights.

---

Transformer-M [22 is Transformer based architecture that that handles multiple molecular data modalities within a unified model by using two separate channels to encode 2D and 3D molecular structures.
SE(3)-DDM [56] leverages an SE(3)-invariant score matching method to transform coordinate denoising into denoising the pairwise atomic distances within a molecule.
3D-EMGP [57 introduces equivariant energy barrgy-based model and develops self-supervised pre- training framework including a physics-inspired node-level force prediction task and a graph-level noise scale prediction task.
proposes pretraining technique based on denoising for 3D molecular structures, showing it is equivalent to learning force field.
Coord 32
hybrid noise strategy by first adding Gaussian noise the dihedral angles of the rotatable bonds, followed by traditional noise to the atom coordinates, with pretraining focused solely on denoising the latter.
Frad (3) introduces
SliDe 31 develops a novel sliced denoising method that adds Gaussian noise to bond lengths, angles, and torsion angles with their variances determined by parameters within the energy function.
molecular pretraining model that uses a two-track transformer to jointly capture atomic-level, graph-level, and geometry-level features, while systematically investigating scaling laws in molecular pretraining.
Uni-Mol2 [16]
CoeE (4 introduces a graph neural network for 3D molecular graphs that adopts rotation angles and local completeness in the 1-hop neighborhood, while integrating quantum-inspiing basis functions into its message passing mechanism.
Mol-AE [19] addresses the gap between pretraining and downstream objectives in encoder-only 3D molecular models by introducing an auto-encoder with positional encodings as atomic identifiers and 3D Cloze Test objective that drops atoms to better capture real substructures.
unifies molecular generation and property prediction through a diffusion- based two-phase process of scaffold nucleation and molecule growth, using a multi-branch network with oversampling to balance tasks.
Uni-GEM 58
D.3 Implementation details
We employ the ID-ReTraans as the 3D graph encoder and implement 2D-PE as 2D-ReTrans, a simplified version of the 3D-ReTrrans that excludes 3D coordinates and distance inputs, while using the Transformer as the structure-independent decoder. The 3D graph encoder configured with a hidden dimension of 256, 8 attention heads, and 12 layers. The 2D-PE shares most of its configuration with the 3D graph encoder, except for hidden dimension of 64 and 4 attention heads. The decoder consists of layers, with the hidden dimension and number of attention heads same the 3D graph encoder. The detailed hyper-parameters configuration for pretraining and finetuning are shown in Table Table 8 and Table respectively.
E
More Experimental Results
E.1 Ablation on Alternative Approach to Eliminating 2D Leakage
We investigate an alternative approach fully eliminate 2D leakage by directly combining 3D embeddings with 2D positional encodings as molecular representations during downstream tasks. We test this setup with SRD both with and without distillation. As shown in Table simply fusing 2D-PE with 3D-ReTrans under SRD without distillation consistently underperforms our original design, highlighting the necessity of SRD with distillation for effective 2D and 3D alignment. In contrast, when distillation is applied, downstream performance becomes insensitive to whether 2D-PF explicitly included, suggesting that pretraining distillation sufficiently aligns the modalities and renders additional 2D input unnecessary.
25

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Table 7: Jypee--aramettrs for pretraining PCQM4MV2.
Parameter
Value
Dataset
PCQM4MV2
Train/Val/Test Split
0thers/100/1000
Batch size
128
Inference Batch size
128
Accumulate grad batches
2
Optimizer
A
Weight decay
le-16
Scheduler
Cosine Annealinn
Init learning rate
5c-5
Min learning rate
Ic-6
Warm up steps
10000
Max epochs
30
Masked ratio
0.25
Masked coordinates reconstruction loss type
MSE loss
Coordinate noise scale(type: Gaussian)
0.04
Denoising loss weight
0.1
Table 8: Hyper- paraae-prraeeters for finetuning on QM9.
Parameter
Value
Dataset
QM9
Train/Val/Test Split Batch size
11000/000//0831
128
Inference Batch size
128
Accumulate grad batches
Optimizer
AdamW
Weight decay
1e-16
Scheduler
CosineAnnealingLR
Init learning rate
5e-4
Min Icarning rate
1c-6
Warm up steps
1000
Learning rate cosine length
2,000,000
Max steps
2,000,000
Max epochs
2000
Finetuning loss type
MSE loss
E.2 Evolution of 2D-PE's representations
To examine how 2D-PE's representation evolves during pretraining, we track the cosine similarity between 2D and 3D representations. As shown in Figure 8 the similarity increases sharply in the initial training steps, approaching 1.0, and then grows gradually, indicating progressive alignment between the two modalities.
26

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Table 9: Hyper-parameters for finetuning MD17.
Parameter
Value
Dataset
MD17
Train/Val Split
9500/500/rmmiiiing data
Batch size
80
Inference batch size
64
Accumulate grad batches
1
Optimizer
AdamW
Weight decay
0.0
Scheduler
Conealnnnl
Init learning rate
5e-4
Min learning rate
le-6
Warm up steps
1000
Max epochs
1200
Force weight
0.8
Energy weight
0.2
Finctuning loss type
MAE loss
Ema alpha dy
1.0
Ema alpha y
0.05
Table 10: Ablation on alternative approach to eliminate 2D leakage. Performance (MAE on MD17.
Downstream Model
SRD
Lasan
Salicylic
Toluene
Uracil
DDD-PE4D--ReTH
/
X
0.0420
0.0293
0.0334
3DD-ReTrans
/
X
0.0416
0.0293
0.0329
PE+3D-ReTTHee
V
0.0384
0.0275
0.0311
3D-Rc ReTrans
V
0.0387
0.0275
0.0315
1.0
Cosine Similarity
0.9
0.8
0.7
0.6
0.5
Cosine Similarity
Ideal Value
2000
4000 6000 8000 100001200014000115000
Steps
Figure 8: Evolution of the 2D-PE's representation.
27


Reviews:
[{"review_id": "oMTuhEGslc", "reviewer": "Reviewer_GqqD", "summary": "This paper addresses a core dilemma in 3D molecular modeling using re-mask decoding: whether or not to leak 2D information into the decoder. The authors propose a clean solution by introducing a student–teacher framework that selectively reveals only the 2D information already learned by the encoder to the decoder. This effectively mitigates the 2D leakage issue. Experimental results on downstream 3D molecular tasks demonstrate the effectiveness of the proposed approach.", "questions": "Could the authors conduct an additional experiment based on Table 1? I'm particularly curious about the performance when using SRD without distillation, but instead representing the molecule with 3D embeddings combined with 2D positional encoding for downstream tasks.\n\nIt would also be helpful to try this setup on your current with-distillation model as well. Since it doesn’t require redoing pre-training, this experiment should be feasible to complete during the rebuttal period.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n\n1, The paper is well-written, with a clear motivation and compelling empirical evidence (e.g., Figure 2) supporting the proposed approach. And all the key claims made early on are later validated through ablation studies.\n\n2, This paper actually effectively addresses two critical challenges: (1) the pre-training vs. downstream mismatch in 3D molecular modeling, resolved by reusing the re-mask technique from 2D models; and (2) the inherent conflicts in re-mask decoding. Both issues are highly relevant to 3D molecular representation learning, and the proposed methods and insights are likely to be influential for the community.\n\n**Weaknesses**\n\n1, My main concern lies in the magnitude of the technical contribution. While the analysis are strong, the technical novelty appears limited. Although the authors claim to use a graph auto-encoder, the model architecture is in fact very similar to transformer-based models already used in the 3D molecular domain, such as Uni-Mol and Mol-AE, both of which are already cited in the paper. In particular, the design is highly similar to Mol-AE, with almost identical architecture and atom drop strategy — the only difference lies in the positional encoding used in the decoder.\n\n2, The use of a distilled PE does not fully eliminate 2D leakage. For example, in an image–text CLIP procedure, we cannot assume that aligned embeddings are completely free of modality-specific information. Additionally, in this paper’s setting, the information of dropped atoms is not present in the 3D encoder output, so their 2D structure is inevitably leaked to the decoder by 2D PE.\n\n3, There exists a much simpler solution to eliminate 2D leakage altogether. During downstream evaluation, you could directly use the 3D embedding combined with the 2D PE as the final molecular embedding—potentially eliminating the need for the distillation design entirely.", "was_revised": true, "final_justification": "The author has already addressed most of my concerns. I still maintain my positive assessment and score.", "rebuttal": "> Weaknesses1: My main concern lies in the magnitude of the technical contribution. While the analysis are strong, the technical novelty appears limited. Although the authors claim to use a graph auto-encoder, the model architecture is in fact very similar to transformer-based models already used in the 3D molecular domain, such as Uni-Mol and Mol-AE, both of which are already cited in the paper. In particular, the design is highly similar to Mol-AE, with almost identical architecture and atom drop strategy — the only difference lies in the positional encoding used in the decoder.\n> \n\n**Response:** \nThank you for your thoughtful comments. We respectfully clarify that our major novelty compared to Mol-AE and UniMol includes: (1) our Selective Re-mask Decoding (SRD) strategy, in which the 2D-PE is a key element; and (2) 3D-ReTrans, which combines the strength of TorchMD-net and Relational Transformer for 3D encoding and 3D masked graph modeling. Here we elaborate their novelties below:\n\n(1) SRD prevents 2D structure leakage in decoder while ensuring 2D structure-informed decoding, which is not addressed in Mol-AE and UniMol. The novelty of SRD lies in:\n* **2D-from-3D Distillation to ensure no structural leakage:** we train 2D-PE via distilled from 3D encoder's representations ensuring its information fully contained within 3D encoder, which is absent in Mol-AE and UniMol.\n* **2D ReTrans-based positional encoding:** we leverage 2D-ReTrans to produce high-quality 2D positional encodings for re-masked decoding. In contrast, Mol-AE relies on SMILES-based positional information, which is less structured and less effective.\n\n(2) 3D-ReTrans outperforms UniMol and Mol-AE, through relational attention and joint scalar and vector features. Both UniMol and Mol-AE employs the Uni-Mol architecture, which can not fully capture interaction between atoms.\n\n**Empirical Validation:** As shown in Table 1, our ablation study on QM9 demonstrates that 3D-ReTrans outperforms both Uni-Mol2 (1.1B) and Mol-AE. The results for Uni-Mol2 are taken directly from [1].\n\n| **Model**     | **$\\mu$** | **alpha** | **R2**  | **Cv**  |\n|---------------|-----------|-----------|---------|---------|\n| Uni-Mol2 1.1B |  0.089   | 0.305   | 5.265   | 0.144   |\n| Mol-AE        | 0.152    |0.434  | 6.962   |0.215    |\n| 3D-ReTrans    | 0.016     | 0.055     | 0.341   | 0.029   |\n\n**Table1: Ablation study on QM9.**\n\n \n\n> Weaknesses2: The use of a distilled PE does not fully eliminate 2D leakage. For example, in an image–text CLIP procedure, we cannot assume that aligned embeddings are completely free of modality-specific information. Additionally, in this paper’s setting, the information of dropped atoms is not present in the 3D encoder output, so their 2D structure is inevitably leaked to the decoder by 2D PE.\n\n**Response:**  Thank you for your concern regarding potential structure leakage. The 2D PE provides 2D structure information, but is not structure leakage, because these information are included in the 3D encoder's embedding. We explain this by giving a formal definition for structure leakage:\n\n* **Definition (Structure Leakage in Masked Graph Auto-Encoders).** In a masked graph auto-encoder, structure leakage refers to the use of any 2D structural information (e.g., adjacency matrix) as input to the graph decoder, unless it is conveyed solely through the graph encoder’s output. Accessing structure only via the graph encoder's output does not constitute structure leakage.\n* _**Definition Explanation.**_ If the decoder has access to structure outside the encoder’s output, it can ignore the encoder, undermining its ability to learn useful representations.\n\n**Why 2D-PE does not constitute structure leakage?** It is learned via 2D-from-3D distillation, i.e.,trained to mimic representations derived from the 3D encoder. As shown in Analysis 5 in our paper, the representation produced by 2D-PE is mostly embedded in the 3D encoder's output. Thus, the decoder does not receive any structural information beyond what is already captured by the encoder, satisfying our definition above.\n\n\n\n> Weaknesses3: There exists a much simpler solution to eliminate 2D leakage altogether. During downstream evaluation, you could directly use the 3D embedding combined with the 2D PE as the final molecular embedding—potentially eliminating the need for the distillation design entirely.\n\n\n**Response:** Thank you for the insightful suggestion. Following your advice, we perform experiments on the MD17 dataset, and present the results below: \n\n|Downstream Model| SRD | L_distill |Salicylic| Toluene  | Uracil  |\n|-------------|---------|-----------|---------|---------|---------|\n| 2D-PE+3D-ReTrans |√  |×  |0.0420  | 0.0293 |0.0334|\n| 3D-ReTrans    | √ |× | 0.0416 | 0.0293 |0.0329 |\n| 2D-PE+3D-ReTrans |√  |√ | 0.0384 | 0.0275| 0.0311  |\n| 3D-ReTrans    | √ | √ | 0.0387 | 0.0275 | 0.0315 |\n\n**Table2: Ablation study on MD17 dataset.**\n\nWe can observe that using our SRD method with distillation present better performance. This demonstrates the benefit of aligning 2D-PE with the 3D encoder during pretraining.\n\n**Why directly training the 2D PE can diminish the role of the 3D encoder in 3D masked graph modeling?** Jointly training the 2D PE with the 3D graph encoder can indeed allow the model to provide 2D structural context to the re-masked atoms. However, without distillation, this setup tends to decouple the learning of 2D and 3D representations: the 2D-PE tends to specialize in capturing 2D topological information, while the 3D encoder focuses solely on 3D geometric features. During pretraining, this separation is less problematic because the decoder has sufficient capacity to integrate both signals. However, this becomes a limitation in downstream fine-tuning, where the fused representation is typically passed through a lightweight MLP projector. The MLP's limited capacity makes it difficult to effectively combine the independently learned 2D and 3D features.\n \n\n> Q1: Could the authors conduct an additional experiment based on Table 1? I'm particularly curious about the performance when using SRD without distillation, but instead representing the molecule with 3D embeddings combined with 2D positional encoding for downstream tasks.\n\n**Response:** Thank you for the question. As shown in Table 2 above, the performance of using SRD without distillation and combing 2D-PE and 3D-ReTrans for downstream finetuning is consitently lower than our original setup with distillation. This highlights the importance of our proposed SRD strategy in aligning 2D and 3D representations for more effective downstream integration.\n\n\n> Q2: It would also be helpful to try this setup on your current with-distillation model as well. Since it doesn’t require redoing pre-training, this experiment should be feasible to complete during the rebuttal period.\n\n**Response:** As shown in Table 2 above, when distillation is applied, the performance is similar regardless of whether 2D-PE is incorporated during downstream finetuning. This suggests that once the 2D-PE is properly aligned through distillation during pretraining, explicitly including it at the downstream stage offers very limited additional benefit.\n\n\n[1] Ji X, Wang Z, Gao Z, et al. Uni-mol2: Exploring molecular pretraining model at scale[J]. arXiv preprint arXiv:2406.14969, 2024."}, {"review_id": "on7MmLFLDh", "reviewer": "Reviewer_Q2ph", "summary": "This paper introduces a novel masked modeling approach for 3D molecular pre-training, together with a dedicated molecule transformer architecture designed to complement this pre-training method. The central contribution is a 3D molecular autoencoder pre-training scheme that addresses the issue of 2D structural information “leakage” to the molecule decoder, which has limited the effectiveness of previous masked molecule modeling frameworks. To tackle this, the authors decouple the 2D graph encoder from the 3D graph encoder, providing masked atoms with 2D structure information exclusively through a separate encoder. This 2D encoder is aligned with the 3D encoder via contrastive learning, which both distills 3D information into the 2D representation and enforces consistency between the modalities. In addition, the paper proposes a new molecule transformer architecture for 3D encoding, inspired by TorchMD-Net but enhanced with 3D data augmentation strategies to promote equivariant learning—an approach that is, to my knowledge, novel in this context.", "questions": "1. Can you report or discuss the computational efficiency of the proposed masked modeling method and the new transformer architecture, in comparison to prior approaches?\n2. A straightforward baseline for the 2D encoder would be to use structural embeddings, as in the GraphGPS paper. Could the authors provide an ablation study comparing their contrastive alignment strategy to such alternatives?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. The authors cleverly noticed the 2D graph structure leakage to the molecule decoder, which is a rather novel observation. Addressing this issue, they used a separate 2D graph encoder to introduce 2D structure information, and apply a structure-unaware molecule decoder. This 2D graph encoder is trained by contrastive learning with the 3D encoder. Overall, I feel the observed challenge is insightful, and the proposed solution is intricate but makes sense.\n2. The method is empirically validated with strong performance on relevant benchmarks, demonstrating improvements over competitive baselines. The experimental section (Section 5) provides supporting analysis for key design choices.\n3. The paper is generally well-written. The technical motivation, architectural decisions, and experimental results are clearly articulated, making the work accessible to the community.\n\n\n**Weakness**\n\n1. The paper would benefit from more detailed analysis and visualization of the learned 2D encoder. For example, does the 2D encoder capture the necessary structural information required by the decoder, and how does this representation evolve during contrastive training?\n2. The 2D-from-3D distillation approach bears some resemblance to the methodology in GraphMVP. A clearer discussion of similarities, differences, and relative advantages compared to GraphMVP would strengthen the paper.\n3. While the proposed molecule transformer is motivated as an advance over relational transformers, its specific improvements over TorchMD-Net are less clear. Further ablation studies isolating the contributions of the new architectural elements (e.g., 3D data augmentations) would be helpful.", "was_revised": true, "final_justification": "I appreciate the authors' responses to my concerns. The additional experiments validating the 2D encoder's capture of structural information effectively address my main concern. The comparison with GraphMVP is also clear, highlighting both similarities and key differences in distillation targets and loss designs. The ablation studies quantify the contributions of innovations and 3D data augmentations. My previous concerns have been addressed with additional experiments and clear explanations, and I have raised my score to 5.", "rebuttal": "> Weaknesses1: The paper would benefit from more detailed analysis and visualization of the learned 2D encoder. For example, does the 2D encoder capture the necessary structural information required by the decoder, and how does this representation evolve during contrastive training?\n\n**Response:** Thank you for the valuable suggestions. We provide additional analysis of the 2D encoder:\n(1) To show whether the 2D encoder capture strctural information required by the decoder, we perform probing experiments. Specifically, we freeze our pretrained 2D encoder and train two MLPs to predict atom types and bond types from its output. The resulting prediction accuracy for both atom and bond types exceed 99.99%, indicating that the 2D encoder effectively encodes strutural information needed by decoding.\n(2) To visualize the evolution of the 2D encoder’s representation, we draw the cosine similarty curve between 2D representation and 3D representation during training. The similarity rises sharply within the first few training steps and approaches 1.0, followed by a gradual increase.\nWe will include these results and visualizations in the final version to improve the clarity and completeness of our analysis.\n\n\n> Weaknesses2: The 2D-from-3D distillation approach bears some resemblance to the methodology in GraphMVP. A clearer discussion of similarities, differences, and relative advantages compared to GraphMVP would strengthen the paper.\n\n**Response:** Thank you for the insightful comment. We provide a clearer discussion of our 2D-from-3D distillation approach and GraphMVP below:\n\n**Similarity:** Both methods leverage information from 2D and 3D graphs to improve representation learning.\n\n**Differences:**\n- GraphMVP transfers 3D information into a 2D encoder to enhance its downstream performance. In contrast, our method distills 3D representation into 2D-PE, ensuring the 2D-PE's embedding is contained within the 3D encoder to avoid structure leakage in decoding.\n- GraphMVP aligns the 2D and 3D views of the same molecule and contrasts the views of different molecules using contrastive losses such as InfoNCE or EBM-NCE. Our method instead uses a cosine similarity loss that encourages 2D-PE to generate structural encodings closely aligned with the 3D encoder, allowing both to advance jointly during pretraining.\n\n**Relative advantages:** Our 2D-from-3D distillation is simpler and efficient, using only a lightweight cosine similarity loss rather than complex contrastive frameworks method to achieve our goal of 2D structure-informed decoding without structure leakage.\n\nWe have revised the related work section to include this discussion.\n\n\n> Weaknesses3: While the proposed molecule transformer is motivated as an advance over relational transformers, its specific improvements over TorchMD-Net are less clear. Further ablation studies isolating the contributions of the new architectural elements (e.g., 3D data augmentations) would be helpful.\n\n**Response:** Thank you for the helpful suggestion. We clarify that our model is not a direct modification of TorchMD-Net. Instead, we borrow its key components (the scalar and vector features) to develop 3D relational-Attention and 3D Update Layer. In addition, unlike TorchMD-Net, which is inherently equivariant, we use 3D data augmentations to encourage 3D-ReTrans to learn 3D equivariance and invariance. This is important because 3D-ReTrans lacks built-in 3D equivariance or invariance.\n\nTo validate the contribution of each component, we conduct ablation studies in Table 1. The results demonstrate that both the architectural innovations and the 3D data augmentation significantly contribute to the model's performance.\n\n\n|Model Components |homo| \n|---------------|---------|\n| Relational-Transformer |27.7 | \n| +Data Augmentation  | 24.6 |\n| +3D Relational-Attention |23.4 | \n| +3D Update Layer(3D-ReTrans) | **22.0** |\n\n**Table 1: Ablation of different components of 3D-ReTrans.**\n\n\n> Q1: Can you report or discuss the computational efficiency of the proposed masked modeling method and the new transformer architecture, in comparison to prior approaches?\n\n**Response:** Thank you for the suggestion. We report the computational efficiency of our method and Frad in the table below. All experiments are conducted on NVIDIA A6000 (48GB) GPUs. Since the Frad paper does not provide GPU-hour estimates, we reproduced their method and approximated the training cost under comparable settings. For pretraining, we use a batch size of 128 and train for 30 epochs. For downstream tasks, we follow this setups: 1500 epochs with batch size 128 for QM9, and 1400 epochs with batch size 100 for MD17. As shown in the table, our method achieves competitive training efficiency compared to prior approaches.\n\n\n|GPU Hours| Pretrain | QM9 | MD17  |\n|----|---------|---------|---------|\n| Frad| 55h  |  50h |8h\n| Ours | 48h | 48h | 8h  |\n\n**Table 2: Computational overhead of our method and Frad.**\n\n> Q2: A straightforward baseline for the 2D encoder would be to use structural embeddings, as in the GraphGPS paper. Could the authors provide an ablation study comparing their contrastive alignment strategy to such alternatives?\n\n**Response:**   Thank you for the susggestion. Following GraphGPS, we conduct pretrain and finetune experiments with the RWSE[1] structure encoding to replace our 2D PE's function. For pretraining, we find that pretrain loss cannot converge as low as the original model. For finetuning, our results are shown in Table 3. Our 2D-PE's performance consistently surpasses the setting using RWSE, indicating the superior of 2D-PE to provide effective 2D structural context.\n\n|2D Encoder| Salicylic | Uracil  |\n|----|---------|---------|\n|RWSE  | 0.0371  | 0.0312 |\n|Ours 2D-PE |  **0.0356**| **0.0292**|\n\n**Table 3:  Results on MD17 dataset.**\n\nWe also show the limitation of RWSE and other kinds of graph structure embedding below:\n1. Neglect of chemical bond types. RWSE primarily encodes structural information such as relative distance and direction between nodes using random walk transition probabilities. However, it neglect chemical bond types and atom types.\n2. Lossy representation. The walk length K in RWSE is fixed and typically cannot be set too large due to computational constraints. This limits the scope of the structural encoding. As a result, RWSE may fail to capture long-range dependencies in complex molecular graphs, leading to incomplete or lossy representations of global molecular structures.\n\n\n[1] Dwivedi V P, Luu A T, Laurent T, et al. Graph neural networks with learnable structural and positional representations[J]. arXiv preprint arXiv:2110.07875, 2021."}, {"review_id": "uN0UbgVuUt", "reviewer": "Reviewer_Pyfo", "summary": "The paper addresses a key concern in masked graph models: an overly expressive decoder can undermine the encoder, leading to suboptimal representations for downstream tasks. Thus, enhancing reconstruction quality under a restricted decoder is crucial.", "questions": "See above.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Pros:\n\n1.The empirical analysis in Section 5 is well-executed and informative.\n\n2.The authors have released the code, promoting transparency and reproducibility.\n\n3.Investigating potential issues with decoders in masked graph modeling (MGM) is a valuable and timely research direction.\n\nCons:\n\n1.The method description lacks clear logic and motivation, making it difficult to understand the rationale behind the design choices.\n\n2.It remains unclear whether the 2D-to-3D distillation is intended to transfer knowledge from 3D to 2D. The authors claim this prevents structural leakage in the 2D encoder. However, as stated in the introduction, GNNs inherently reveal structural information. Thus, the motivation for distillation is ambiguous, and the distilled knowledge appears to implicitly encode structural information.\n\n3.Figure 3 is unclear and unintuitive—at first glance, it is not obvious that SRD represents a distillation mechanism. Is it trained jointly with the reconstruction module? This should be clarified.\n\n4.In the related work section, classifying GraphMVP and 3D InfoMax as 2D molecular graph pretraining methods may be inaccurate, as both are fundamentally 3D-based approaches.\n\n5.The two challenges outlined in the introduction have already been discussed in prior work, such as SimSGT. It is unclear what novel perspective or contribution this paper offers in comparison.\n\n6.The baseline methods used in the experiments are somewhat outdated, with most comparisons limited to works from around 2023.\n\n7.The paper appears to introduce a new 3D encoder, 3D-ReTrans, which contradicts the claim of encoder-agnosticism. What happens if existing encoders are used instead? How does this encoder differ from prior works? Additionally, the reported performance differs from results in the original papers:\n\n[1] Spherical Message Passing for 3D Molecular Graphs\n\n[2] ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs\n\n[3] A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks", "was_revised": true, "final_justification": "Thank you for the authors' efforts during the rebuttal period, including the additional experiments. This paper investigates the often-overlooked capabilities of the decoder in Masked Graph Models, providing valuable insights for the community.", "rebuttal": "> Q1: The method description lacks clear logic and motivation, making it difficult to understand the rationale behind the design choices.\n\nThank you for the question. We would like to elaborate on the core logic and motivation behind our core design **Selective Re-mask Decoding(SRD)**:\n\n**Goal:** We aim to adapt re-mask decoding for 3D masked graph modeling to better align the reconstruction objective with representation learning (see Lines 34–44 in the introduction).\n\n**Core Observation:** Existing methods face a dilemma:\n* To improve reconstruction quality, some models leak ground-truth 2D structure to the decoder. This harms the encoder’s ability to learn meaningful representations.\n* Others avoid structure leakage by using a structure-agnostic decoder, but this makes reconstruction under re-mask decoding very challenging—since the decoder cannot infer the relative positions of multiple masked atoms.\n\nTo formalize this issue, we define:\n* **Definition (Structure Leakage in Masked Graph Auto-encoders).** In a masked graph auto-encoder, structure leakage refers to the use of any 2D structural information (e.g., adjacency matrix) as input to the graph decoder, unless it is conveyed solely through the graph encoder’s output. Accessing structure only via the graph encoder's output does not constitute structure leakage.\n* _**Definition Explanation.**_ If the decoder has access to structure outside the encoder’s output, it can ignore the encoder, undermining the encoder's ability to learn useful representations.\n\n**Our Idea:** To resolve the challenge, we propose SRD: \n* Applies standard re-masking to atom representations;\n* Add the re-masked representations with a 2D positional encoding (PE);\n* Crucially, this 2D PE is trained via distillation to mimic the 3D graph encoder’s output. In this way, the 2D PE does not provide information beyond the 3D encoder's output, thus avoiding structure leakage.\n\nWe have revised the method section to better emphasize this logic, and we hope this clarifies our motivation and design choices.\n\n> Q2: The motivation for distillation is ambiguous, and the distilled knowledge appears to implicitly encode structural information.\n\nTo answer the question, we explain that **the 2D PE can provide structural information, but are not structure leakage because these information are mostly contained in the 3D encoder.**\n\n* **2D-to-3D distillation ensures that the 2D PE's embedding are mostly contained by the 3D encoder's output.** 2D-PE's representations are supervised by 3D encoder outputs through distillation. In Analysis 5, we empirically show that the 2D-PE's representations can be mostly recovered from the 3D encoder's output (nearly 1.0 cosine similarity).\n* **By definition, structure information provided by the 3D encoder is not leakage.** Please refer to our response to W1 for the definition of structure leakage in masked graph auto-encoders. Considering the embedding provided by the 2D-PE is mostly contained in the 3D encoder's output, they are not structure leakage.\n\n> Q3: Figure 3 is unclear and unintuitive—at first glance, it is not obvious that SRD represents a distillation mechanism. Is it trained jointly with the reconstruction module? This should be clarified.\n\nTo clarify, Selective Re-mask Decoding (SRD) includes two components that work together during training: \n* **Re-mask decoding with 2D PE.** The decoder is trained to reconstruct masked atom positions using both the 3D encoder output and 2D-PE representations. The 2D-PE provide necessary 2D structural context, enabling the decoder to distinguish the relative positions of re-masked atoms for better reconstruction.\n* **2D-from-3D distillation.** This is a joint loss function with the reconstruction module, aiming to train the 2D-PE. Specifically, 2D-PE is trained via knowledge distillation from 3D encoder, using cosine similarity loss over unmasked atoms. The stop-gradient operation can enforce 2D-PE to foucus on learning from 3D encoder's representation and 3D encoder to specialize in representation learning, respectively. The total pretraining loss is the sum of the MSE loss and the consine similarity loss.\nWe have revised our Section 4.1 and Figure 3 to make this clearer.\n\n> Q4: In the related work section, classifying GraphMVP and 3D InfoMax as 2D molecular graph pretraining methods may be inaccurate, as both are fundamentally 3D-based approaches.\n\nWe initially categorized GraphMVP and 3D InfoMax as 2D molecular graph pretraining methods because they focus on enhancing 2D encoders using 3D information and apply 2D graphs in downstream tasks. However, we agree it is more accurate to classify them as 3D pretraining methods due to their reliance on 3D signals during training. We have updated the related work section accordingly.\n\n> Q5: The two challenges outlined in the introduction have already been discussed in prior work, such as SimSGT. It is unclear what novel perspective or contribution this paper offers in comparison.\n\nWe respectfully clarify that the two challenges introduced in our paper are fundamentally distinct from those addressed in SimSGT:\n* **Challenge 1** is about the 2D structure leakage in the graph decoder, a phenomenon that is neither discussed nor mitigated in SimSGT.\n* **Challenge 2** concerns reconstructing masked graphs without introducing 2D structure leakage—a capability not explored in SimSGT.\n\nBeyond these conceptual differences, our work also introduces several key technical novelties and differences:\n\n- **Selective Re-mask Decoding (SRD):** Our SRD strategy explicitly prevents 2D structure leakage by controlling the flow of 2D information into the graph decoder. This contrasts with SimSGT’s re-masking scheme, which still allows such leakage.\n*\t**3D-ReTrans Architecture:** We propose a novel 3D graph encoder-decoder architecture designed for 3D masked graph modeling. This architectural design is not present in SimSGT or other prior work.\n\n**Different Problem Scopes:**  Our study focuses on 3D masked graph modeling, whereas SimSGT addresses only the 2D setting. This difference in scope further differentiates the contributions of our work.\n\nIn summary, both the challenges we define and the solutions we propose are orthogonal to those in SimSGT, and collectively constitute a novel contribution to the field.\n\n\n> Q6: The baseline methods used in the experiments are somewhat outdated, with most comparisons limited to works from around 2023.\n\nIn response, we have added a recent 2025 baseline UniGEM[1] in our revised manuscript to ensure a fairer comparison. As shown in Table 1, our method outperforms UniGEM across 6 tasks in QM9, further validating its effectiveness.\n\n|Model| α | homo | lumo  | gap| μ  |Cv\n|---|---|---|---|---|---|---\n|UniGEM| 0.060 | 20.9 |16.7| 34.5| 0.019| 0.023\n|3D-GSRD | **0.038** |**18.0** | **14.5** |**31.1** |**0.009**|**0.020**\n\n**Table 1: Results on QM9 dataset.**\n\n> Q7.1: The paper appears to introduce a new 3D encoder, 3D-ReTrans, which contradicts the claim of encoder-agnosticism. What happens if existing encoders are used instead? \n\nWe introduced 3D-ReTrans because it achieved the best performance in our experiments, but our core contribution, the SRD mechanism, is itself encoder-agnostic. To verify the encoder-agnostic nature of our method, we conducted additional experiments using the Relational Transformer in place of our proposed 3D-ReTrans. The results, presented in Table 2, demonstrate that adding SRD  consistently improves performance. This shows that SRD, as a general pre-training strategy, can empower different 3D encoder architectures.\n\n|Decoder| SRD | L_distill |Toluene  | Uracil \n|---|---|---|---|---\n|Structure-dependent |√  |√  |0.1144 | 0.0813 \n|Structure-independent | × |× |0.1250| 0.0828\n|Structure-independent |√  |× |0.0998 | 0.0843  \n|Structure-independent | √ | √ |0.0745 | 0.0733 \n\n**Table 2: Results on Relational-Transformer encoder.**\n\n> Q7.2: How does this encoder differ from prior works? \n\nHere we summarize the key innovations and differences of our encoder over several major prior works:\n\n**Relational Attention.** Unlike standard Relational Attention, which directly injects pair features into attention weights, we separate and jointly process scalar features (e.g., atom types and distances) and vector features (e.g., directional geometry). Our 3D Relational-Attention captures pairwise interactions and integrates them into both scalar and vector channels, followed by a 3D Update Layer that enables interaction between scalar and vector features.\n\n**TorchMD-Net.** TorchMD-Net's attention mechanism primarily include interatomic distances. We go further by incorporating edge vectors into attention. These are concatenated with node vectors before QKV projection.\n\n**Uni-Mol.** Uni-Mol uses separate atom and pair representations, connected via static attention bias. In contrast, we dynamically process scalar and vector features with cross-channel interaction, enabling more expressive modeling of molecular geometry and structure.\n\n> Q7.3: Additionally, the reported performance differs from results in the original papers:\n\nThe SphereNet [2] results reported in our paper follow those provided by Frad [5]. We acknowledge that these may differ from the original paper due to differences in reproduction settings. To address this, we have updated our manuscript to include the original SphereNet results from [2]. Additionally, we appreciate your suggestion and will include [3] and [4] as additional baselines in the final version to ensure a more comprehensive comparison.\n\n[1] UniGEM: A Unified Approach to Generation and Property Prediction for Molecules. ICLR 2025\n\n[2] Spherical message passing for 3d molecular graphs\n\n[3] ComENet: Towards complete and efficient message passing for 3D molecular graphs\n\n[4] A new perspective on building efficient and expressive 3D equivariant graph neural networks\n\n[5] Fractional denoising for 3d molecular pre-training"}]

Author rebuttal:

Meta-review: This paper proposes a selective re-mask decoding strategy for 3D molecular representation learning. To this end, the authors focus on solving the 2D structural leakage issue, where the encoder fails to learn meaningful representations due to the decoder being able to fully reconstruct the 3D coordinates from 2D structures. 

Reviewers were concerned about the novelty of neural architecture and that the use of distilled 2D position encodings may not completely eliminate structure leakage. The rebuttal have somewhat resolved these concerns. 

Overall, while the conceptual advance is modest, the paper is technically sound, demonstrates clear empirical benefits, and provides meaningful insights into the design of 3D molecular pretraining methods. I recommend acceptance for the paper.
Decision: Accept (poster)