UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Authors: Ruifeng Li
Mingqian Li
Wei Liu
Yuhua Zhou
Xiangxin Zhou
Yuan Yao
Qiang Zhang
Hongyang Chen
Venue: ICLR 2025 Spotlight (2025)
Keywords: Few-shot molecular representation learning
maching learning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We introduce HierMatch, which performs matching across multiple levels, from atoms to tasks, to enhance molecular property predic- tions in few-shot learning scenarios.

Abstract: Drug discovery is crucial for identifying candidate drugs for various diseases. However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates explicit hierarchical molecular matching with implicit task-level matching via meta-
learning, bridging multi-level molecular representations and task-level generalization. Specifically, our approach explicitly captures structural features across multiple levels—atoms, substructures, and molecules—via hierarchical pooling and matching, facilitating precise molecular representation and comparison. Additionally, we employ a meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular alignment while leveraging shared meta-knowledge for fast adaptation. Our experimental results demonstrate that UniMatch outperforms state-of-the-art methods on the MoleculeNet and FS-Mol benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in ∆AUPRC. UniMatch also shows excellent generalization ability on the Meta-MolNet benchmark.

Published as a conference paper at ICLR 2025
UNIMATCH: UNIVERSAL MATCHING FROM ATOM TO TASK FOR FEW-SHOT DRUG DISCOVERY
Ruifeng Li Mingqian Li?, Wei Liu', Yuhua Zhou Xiangxin Zhou',
Yuan Yao 1,,, Qiang Zhang!.. 1.51 Hongyang Chen
Zhejiang University Zhejjaang Lab Shanghai Jiao Tong University
University Chinese Academy Sciences
5 ZJU-Hangzhou Global Scientific and Technological Innovation Center
(lirf, qi giang. zhang. es } @zju. edu. en, dr.h. chen@ eee. org
ABSTRACT
Drug discovery is crucial for identifying candidate drugs for various discases. However, its low success rate often results in a scarcity of annotations, pos- ing few-shot learning problem. Existing methods primarily focus single- scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates ex- plicit hierarchical molecular matching with implicit task-level matching via meta- learning, bridging multi-level molecular representations and task-level general- ization. Specifically, our approach explicitly captures structural features across multiple levels- atoms, substructures, and molecules-vis hierarchical pooling and matching, facilitating precise molecular representation and comparison. Ad- ditionally, we employ meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular align- ment while leveraging shared meta-knowledge for fast adaptation. Our exper- imental results demonstrate that UniMatch outperforms ofate-of-the-art meth- ods on the MoleculeNet and FS-Mol benchmarks, achieving improvements 2.87% in AUROC and 6.52% in AAUPRC. UniMatch also shows excellent gen- eralization ability on the Meta-MolNet benchmark. The code is available at https:// htt//gtttuuut thub. com wwt.m//irainnnn/nm wtw.:a/2////MMMMM..hhhch
INTRODUCTION
Drug discovery is pivotal for human health, involving the screening and optimization of numer- compounds to identify potential drug candidates that satisfy both pharmacological efficacy and toxicological safety criteria (Drews, 2000; Renaud et al., 2016; Atanasov et al., 2021). The tra- ditional drug development cycle typically spans over decade, incurs costs exceeding $1 billion, yet achieves a success rate of less than 10% (Sliwoski et al., 2014; Adelusi et al., 2022). Artificial Intelligence-Drivee Driven Drug Discovery (AIDD) has emerged as a promising solution address this challenge (Mak et al., 2023; Macalino et al., 2015; Gawehn et al., 2016). Within AIDD, Quantita- tive Prructure-ActivityyPPoo
Relationships (QSAR/QSPR) (Cherkasov al., 2014; Liu & Long, 2009) models are crucial for predicting the relationships between molecular structures and their ac- tivities. These methods rely heavily on extensive datasets due to the complexity of understanding and modeling molecular geometries (Zhang et al., 2021 Fabian et al., 2020; Wang et al., 2021; Chen al., 2023). However, the lengthy durations, high costs, and low success rates of chemical wet experiments limit the availability of labeled experimental data.
Few-shot learning (Li et al., 2023; Song et al., 2023) has demonstrated substantial potential in ad- dressing data scarcity by enabling models to generalize rapidly from minimal data to new tasks. Recently, several approaches have been proposed to address this challenge in few-shot scenarios (Wang et al., 2020). Most approaches are based molecular graphs with atoms nodes and chemical bonds as edges, leveraging Graph Neural Networks (GNNs) (Zhou et al., 2020) to cap- ture molecular topologies. In particular, such models as IterRefl.STe STM (Altae- Tran et al., 2017),

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Atom
Substructure
Molecule
Hydrogen Fluoride
Ammonia
Dodecane
Ethanol
Water
Carbon dioxide
NH
Acidity
Hydrophobicity
0
1
Boiling point
Fluorine and nitrogen affect molecular acidity and basicity, respectively.
Hydroxyl groups affect the molecular hydrophobieity,
The whole structures affect the molecular boiling point.
(b)
Different levels of molecular structures affect different properties: (a) at the atomic level, fluorine and nitrogen affect molecular acidity and basicity, respectively; (b) at the substructural level, hydroxyl groups affect the hydrophobicity ethanol and dodecane; and (c) at the molecular level, the overall structures influence boiling points. Key molecular structures are highlighted in red.
Figure 1:
Meta-MGNN (Guo et al., 2021), PAR (Wang et al., 2021), ADKF-IFT (Chen et al., 2023), and Meta-GAT (Lv et al., 2024) employ GNNs as encoders to learn molecular representations for label prediction. Additionally, several sequence-ber based methods, such as CHEF (Adler et al., 2020). MH- Nfs (Schimunek et al., 2023) and CRA (Li al., 2024), utilize Multilayer Perceptrons (MLPs) as encoders to compress molecular fingerprints or descriptors for predictive modeling.
However, existing approaches often overlook a crucial aspect: different levels of structural information-ranging from atoms to substructures to the entire molecule-contributt to dis- tinet molecular properties. Some properties are influenced by atomic composition, while others depend substructures the overall molecular configuration. Figure illustrates this with ex- amples: (a) fluorine and nitrogen affect molecular acidity and basicity, respectively; (b) hydroxyl groups influence the hydrophobic properties of ethanol and dodecane; and (c) the overall molecular structure affects boiling points. In graph-based methods, the use of multiple GNN layers may cause over-smoothing. where the receptive fields of nodes expand excessively, thus obscuring substructural details (Chen et al., 2020). As a result, GNNs are more suitable for predicting properties related to the overall structure of molecules. In contrast, fingerprint- bassed methods rely on only fragmented local features, potentially overlooking critical information about the overall molecular structure. Al- though CHEF (Adler al., 2020) introduces representation fusion strategy, its reliance on ECFP6 (Rogers & Hahn, 2010)-which which is based on fixed local features limits its ability to capture hierar- chical molecular structures. Therefore, effectively capturing different levels of molecular structures is crucial for accurately predicting wide range of molecular properties.
To address this challenge, we propose Universal Matching Networks (UniMatch), a framework that facilitates universal matching across multiple levels-fron atoms to tasks enhancing the few- shot molecular property prediction task. Our main contributions are summarized as follows:
To the best our knowledge, we are pioneers to introduce a universal matching approach that spans from the atomic level the task level. This framework employs explicit hierar- chical molecular matching and implicit task-level matching at distinct levels to align molec- ular structures with tasks. The dual matching mechanism complements itself, forming a synergistic framework that enhances the model's adaptability and generalization across various tasks (Section 3).
We propose an explicit hicrarchical molecular matching mechanism that integrates infor- mation from atoms higher-level structures, capturing complex molecular features. By utilizing an attention-based matching module, the model aligns representations across mul- tiple levels, selecting the most relevant features for improved prediction (Sections 3.1).
We incorporate a meta-learning strategy to achieve implicit task-level matching, learn- ing shared parameters that generalize across tasks. This matching occurs at abstract level, capturing task similarities through optimization and enabling rapid adaptation and improved generalization (Section 3.2).
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Our UniMatch outperforms state-of-the methods on both the MoleculeNet (Section 4.1) and FS-Mol (Section 4.2) benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in AAUPRC, respectively. Additionally, evaluate the generalization ability of UniMatch on the Meta-Mol benchmark, where it demonstrates outstanding performance (Section 4.3).
2
BACKGROUND
2.1 PROBLEM DEFINITION
The few-shot molecular property prediction problem, defined by ADKF-IFT (Chen et al., 2023) and MHNf' (Schimunek al., 2023), involves training models on of tasks {T, {T sampled from the training set Dirgin to improve generalization to new tasks. Each task T, includes support set S, ((x+,á (= N2 and query 2- ((x+.j. )ーー)) where X,,i E Rd and x+ IRd represent molecular features, and fr.i, Vr.j E {0. 1} indicate the molecular properties or activities. The support set S, provides few labeled examples for task-specific adaptation, while the query set 2 utilized to evaluate the model's performance on unseen examples.
2.2 PRELIMINARIES
Graph neural networks (GNNs) designed to handle graph-structured data (non-Euclidean data) by aggregating information from neighboring nodes to learn effective representations (Zhou et al., 2020). Models such GCN (Kipf & Welling, 2017), GIN (Xu et al., 2019), and GAT (Veličković et al., 2018) are widely used for graph classification and other related applications. In a graph G = [V,E), V represents the set of nodes, and & the set of edges. h represents the initial features of node u, and b denotes the features of the edge Cu,e between nodes u and e. At the 7 layer, the representation h!! of node is updated in GNNs follows:
h = UPDATE (n--") ", AGGREGATE") (((n)-)) "ool-",n@-"" hh-1), br...) (eE(()))) (1)
where ((v) the set of neighboring nodes of u. The AGGREGATE function combines features from neighboring nodes, and the UPDATE function updates the node features for the next layer.
METHOD
This section presents the model architecture (Section 4.1) and the meta-learning strategy (Section 4.2) of Universal Matching Networks, which respectively achieve explicit hierarchical molecular matching and implicit task- matching. Figure 2 illustrates overview of UniMatch framework.
3.1 ARCHITECTURE: EXPLICIT IERRRCIICCAL MOLECULAR MATCHING
We propose a novel architecture for explicit hierarchical molecular matching, which captures and aligns complex molecular structures across multiple levels (atomic, substructural, and molecular) via hierarchical pooling and matching, enabling fine-grained comparison and similarity assessment from local to global scales. It establishes the foundation for implicit task-level matching.
Encoding Module. Following the mainstream graph-based few-shot molecular property predic- tion approaches (Wang al., 2021; Guo et al., 2021; Lv et al., 2024; Chen al., 2023), adopt the widely used GIN (Xu et al., 2019) as the backbone of our method. In GNNs, each layer ag- gregates local information from nodes and their neighboring hops. As the network depth increases, the model incrementally aggregates hierarchical information, progressing from individual nodes to substructures and ultimately capturing the entire molecule.
To capture molecular representations at different levels, we employ mean pooling to aggregate node representations at each layer of the GNN. For given task r, we first obtain the node representations
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General parameter
Prediction
Task-spezific parameter
Labels
Fusion
Prediction
Molecular representation
Matching module
+J
Concar.
Encoding module (left)
Encoding module (right)
Matching
GNN layer
Pooling
Pooling
GNN layer
module
COC0
Shared
Scaled dot-
Matching
product attention
GNN layer
Pooling
Pouling
GNN layer
module
Linear
Linear
Linear
Shared
Query
Support
Labels
Active
Active
Inactive
Inactive
Query
Support set
Our model follows hierarchical mooling-matching architecture comprising two components: an encoding module (including pooling) and matching module. First, mean pooling applied at each GNN layer to generate multi-level molecular repre- sentations. Then, attention mechanism is utilized to align representations between the support set and query set across different levels. Finally, predictions from different GNN layers are integrated to obtain the final results. Right: The detailed process of the matching module.
Figure 2: The overview of UniMatch. Left:
Re for the support set S, and b E Ro,xd for the query set 2,. Then, we utilize mean pooling to derive the molecular representations z E Nedd for the support set S, and 27.0 Ny for the query set Qr, as follows:
Pooling(( (h (2) F.a.et v E Vr.s), 11 Tg Pooling(h!") r.g.v v Vr.g). where the Pooling function denotes mean pooling, and refers the 1th layer of the GNN.
Matching Module. Hierarchical matching plays a critical role in our approach, as it allows the model to capture structural details at different levels-from atoms entire Pnrecules-ensuriie a more accurate and fine-grained similarity identification. To achieve this, we adopt the attention mechanism introduced by Vaswani et al. (2017), which dynamically weighs the contributions of molecular features at multiple scales. Specifically, we designate the molecular representations z E N xd in the support set S, the key, and the molecular representations 2!4) NT in the query set Q, the query. The corresponding ground-trume truth labels yr,, E N in the support set serve as the value. This attention-based approach allows for precise matching at the specified level:
1 T. = Softmax
(Zr.y W (z). W
(ZT.
Jy
(3)
Vd
where d is the dimension of molecular representations, and W W, Rddd
Fusion. We repeat the steps above to obtain the matching results y Fig for each GNN layer. These results are concatenated into joint representation, which is then passed through linear layer to generate the final prediction 8r.g E R2 N?2,
Sr.g Linearw. Linearw,(Concale (Concat() (2)
7.g1
where I. represents the total number of layers in the GNN, and W, E Lx2 are the parameters of the Linear function. For classification tasks, Fr,q is normalized into probability distribution using Softmax. By fusing multi-level features, the model captures the complex relationships within molecular structures, resulting in more robust and comprehensive predictions.
'hhe subscript represents it helongs to support set.
the subscript q represents belongs to query set.
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3.2 META-
We employ meta-learning strategy to facilitate implicit task-level matching. To further clarify why meta-learning functions as implicit task-level matching, we introduce a task relationship matrix that captures task similarities at a higher level of abstraction. This matrix allows for efficient adaptation and enhances generalization through optimized learning-
3.2.1 TRAINING AND INFERENCE
For simplicity, we define UniMatch as Sow. where 0 denotes the parameters of the molecular en- coder, and w (Wa: Ws, Wo) represents the parameters of the matching and fusion modules.
Training Phase. During the training phase, we employ a standard meta-learning process to im- prove the model's generalization ability using the training set Duains illustrated in Algorithm 1. Parameter learning involves combination of inner and outer optimization. To facilitate dual op- timization, split the support set Smain, for each task T into S and O,- The training loss C (( fe.w) evaluated on Q is defined as:
L (O,,
Σ (x.
-y log (8r.),
(5)
where y E R is a one-hot vector representing the class of the sample, where a positive sample is denoted by [1, O] and negative sample is denoted by [o, 1].
Inner Loop. During the inner optimization, task-specific parameters w, are updated for each task T, enabling the model to adapt quickly to the current task, as follows:
= a W (Q,, fe.w),
where 0 denotes the fixed parameters in the inner optimization and the inner learning rate.
Outer Loop. The outer optimization aims to update the meta-parameter (0 and w) improve generalization across all tasks. During training, this is achieved by minimizing the aggregated loss over all tasks:
0" w arg min 0.w
(Quramn, fo.w-),
where N₂ is the total number of training tasks.
Inference Phase. After training, we evaluate the model on set of test tasks Tiss drawn from the test set Drest- The support set S split into S and Q". With fixed, we fine-tune the task- specific parameters using Q" as defined in Eq. 6. After fine-tuning, the model is evaluated on Swstr, which serves as the support set to predict the labels for the unknown query molecules.
3.2.2 IMPLICIT
MATCHING
In our UniMatch framework, we consider meta-learning nism. During the training phase, the model autonomously captures and internalizes inter-task rela- tionships and features, embedding them into its meta meta-paramn paraaaarrreesss.... As result, even without explicitly modeling task relationships, the model can rapidly adapt to new tasks, demonstrating a form of im- plicit matching.
implicit task-level matching mecha-
Implicit matching primarily captures the model's 's adaptability across multiple tasks through latent relationships, rather than direct parameter updates. To quantify these relationships, define task relationship matrix M E N.NN, where each element Mi,j represents the relationship (c.g., similarity or distance) between task Timin, and task Tiiin.j which defined follows:
Ma = 90.w (Trrin, Tirin.
where 90. w is a relation function based on the meta- between task T and task Tirin..j
meta- parameter (0 and w), measuring the relationship

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Inner Loop. We further extend the concept of implicit matching directly shape the represen- tation of task-specific adaptation parameters w,- traditional explicit training, task parameters w, are determined via gradient updates of the peraa-arr as shown in Eq. 6. By contrast, implicit matching enables new formulation these parameters as follows:
W. W. L M (w, wr).
This approach replaces explicit gradient updates with implicit parameter matching, aligning task- specific parameters to the optimal solution based on inter-task relationships.
Outer Loop. After completing the inner-loop training, the implicit relationship matrix M is updated. Using the updated M, the general parameter further refined. The update formula can be rewritten
-= 0
M.j (w3 ws).
(10)
where 7) is the outer learning rate. This formula uses M to guide parameter updates, emphasizing the core role of implicit matching in training phase. Simultancously, the task-specific parameters w can be updated using Eq.
Further details on the training and inference phase are provided in Appendix A.
EXPERIMENT
In this section, we evaluate the empirical performance of UniMatch, as outlined in Section We validate our UniMatch on the MoleculeNet (Section 4. and FS-MoJ (Section 4.2) benchmarks. Additionally, we perform an ablation study UniMatch in Section 4.2. To demonstrate the gen- eralization of UniMatch, Section 4.3, covering both single-task and multi-task scenarios. Finally, we conduct visualization experiments in Section 4.4 to demonstrate the importance of the dual matching mechanism in Uni- Match. Additional experiments and analyses are provided Appendix E.3. All experiments are run an NVIDIA RTX A6000 GPU.
further test
datasets from the Meta-MolNet benchmark in
4.1 FEW-SHOT MOLECULAR PROPERTY PREDICTION ON THE MOL.CULENET BENCHMARK
Table 1: Comparison of all methods the MoleculeNet benchmark with support set size of 20.
The mean test performance is reported as AUROC% along with the standard deviations.
Method
Tox21 (12)
SIDER (27) t
MUV (17)
ToxCast (617) t
CHEF (Adler et al., 2020)
61.97 + 0.65
57.34
0.82
53.17 + 4.21
56,52 + 1,24
MixHop (Abu-El-Haile et al., 2019)
78.14 + 0.33
72.01 + 0.87
78.04 + 3.01
77.19 = 0.93
Siamese (Koch et al., 2015)
80.40 + 0.35
71.10 1110):0 4.32
59.59 5.13
ProtoNet (Snell al., 2017)
74.98 0.32
64.54
65.88
63.70 + 1.26
MAML (Finn et al., 2017)
80.21 0.24
0.76
66.79 1 0.85
TPN (Liu et al., 2018)
76.05 0.24
65.22 0.98
62,74 1 1.45
EGNN (Kim et al., 2019)
81.21
72.87
-.73
65.20 2.08
63.65 = 1.57
IteRRefI.STM (Altae-Tram et al., 2017)
0.17
69.63 1 0.31
45.56 4.12
PAR (Wang al., 2021)
82.06
74.68 + 0.31
66.48 2.12
69.72 1: 1.63
ADKF-IFT (Chen al., 2023)
0.60
67.72 1.21
98.18 3.05
72.07 = 0.81
MHNFss (Schimunek et al., 2023)
0.82
65.89 1.17
2.53 73.81
74.91 + 0.73
UniMatch (Ours)
82.62 0.43
68.13 + 1.54
79.40 3.14
77.74 + 0.75
Pre-GNN (Hu et al., 2020)
82.14
73,96 1 0.08
67.14 1.58
73.68 - 0.74
GNN-MAML. (Guo al., 2021)
82.97 + 0.10
0.21
68.99
1.84
Pre-PAR (Wang et al., 2021)
84.93 +.11
78.08 0.16
69.96 + 1.37
75.12 + 0.84
Pre-ADKF-IFT (Chen et al., 2023)
86.06 1 0.35
70.95 0.60
95.74 0.37
76.22 1 0.13
Pre-UniMatel (Ours)
86.35 + 0.13
80.34 + 0.45
86.35 0.76
81.63 = 0.73

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0.35
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0.206
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AAUPRC
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AAUPRC
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0.155
0.161
0.20
MHNfs
CHEF
ProtoNet
MAT
0.103
0.095
o 201
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PAR
GNN-ST
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MixHop
GP-ST
Meta-leaming Hier, Poeing lierr, Matching Fooling Matching
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ADKF- FT
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wro Hiar Matching
GNN MT
UniMatch
UniMaatch
0.00
GIN
GCN 16 support size
GAT
GraphSAGE
Support set size
Figure 3: Mean performance with standard errors on the FS-Mol test tasks. (a) Performance of all compared approaches on the FS-Mol benchmark. (b) Ablation study of the dual matching mecha- nism in UniMatch across different backbones.
Benchmark and Baselines. MoleculeNet (Wu et al., 2018) serves as benchmark for few-shot molecular property prediction, focusing on small molecules with molecular weights below 900 Dal- tons. This benchmark includes 4 datasets-Tox21, SIDER, MUV, and ToxCast-whice contain 12, 27, 17, and 617 tasks, respectively. We compare UniMatch with two types of baselines: 1) Methods trained from scratch, including CHEF (Adler al., 2020), MixHop (Abu-El-Haija al., 2019), Siamese (Koch et al., 2015), ProtoNet (Snell et al., 2017), MAML (Ren et al., 2018), TPN (Liu et al., 2018), EGNN (Kim et al., 2019), IterRefI.STM (Altae-Tran et al., 2021), MHNIs (Schimunek et al., 2023), and ADKF-IFT (Chen et al., 2023); 2) Methods that fine-tune pretrained models, including Pre-GNN (Hu et al., 2020), GNN-MAMI (Guo al., 2021), Pre-PAR (Wang et al., 2021), and Pre-ADKF-IFT (Chen et al., 2023). Pre-Unine Pre-UniMatch is UniMatch that utilizes pretrained parameters from the Pre-GNN model (Hu et al., 2020). More details are provided in Appendix B.
al., 2017), PAR (Wang
Evaluation Procedure. Following the procedural framework Wang et al. (2021). we adopt AU- ROC (the area under the receiver operating characteristic curve) as the evaluation metric and set the support set size at 20 (i.e., 2-way 10-shot). The model trained using the Adam optimizer (Kingma & Ba, 2014). During testing, results are averaged from 10 repeated experiments with different ran- dom seeds. For the baselines, we replicate the results for CHEF, MixHop, and MHNfs, while results for the other baselines are cited from Chen al. (2023). Further details of the experimental setup can be found in Appendix B.2.
Performance. Table demonstrates that both UniMatch and Pre-UniMatch outperform exist- ing state-of-the-art methods on the Tox21, SIDER (pre-training stage only), and ToxCast datasets, achieving an average improvement of 2.87%. Compared to CHEF, UniMatch demonstrates superior performance, suggesting that graph structures are more effective than fixed fingerprints for hierar- chical representation learning in this context. Additionally, our UniMatch outperforms MixHop, highlighting the importance of hierarchical matching for molecular property prediction, especially in few-shot scenarios. On the MUV dataset, UniMatch ranks second among all baselines, possibly due to the severe distribution imbalance inherent in the MUV dataset.
4.2 FEW-SHOT MOLECULAR PROPERTY PREDICTION ON THE FS-MOL BENCHMARK
Benchmark and Baselines. FS-Mol, introduced by Stanley et al. (2021), serves as benchmark
for few-shot molecular property prediction. comprises 5, 5,120 tasks, partitioned into 4,938 for train- ing, 40 for validation, 157 for testing, covering total of 233,786 compounds (see Appendix C.1). To evaluate UniMatch, we compare it against four types of baselines: 1) Single-task methods: single-task GP with Tanimoto kernel (GP-ST) (Ralaivola et al., 2005), single-task GNN (GNN- ST) (Gilmer et al., 2017), MixHop (Abu-El-ae A-bu-El-Haija et al., 2019), and CHEF (Adler et al., 2020);
2) Multi-task pre-training: Multi-task GNN (GNN-MT) (Stanley et al., 2021); 3) Self-supervised pre- tre-training: Molecule Attention Transformer (MAT) (Maziarka et al., 2020); and 4) Meta-learning methods: PAR (Wang et al., 2021), ProtoNet (Snell al., 2017), GNN-MAMI. (Guo al., 2021),
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ADKF-IFT (Chen et al., 2023), and MHNfs (Schimunek al., 2023). Further details can be found in Appendix C.2.
Evaluation Procedure. We follow the experimental setup of the FS-Mol benchmark (Stanley et al., 2021). For each task, we employ unbalanced sampling to create an uneven distribution of positive and negative samples within the support set. The evaluation metric, AAUPRC, is used to effectively assess the model's ability to improve minority class prediction, which is critical in im- balanced datasets (see Appendix C.3). During testing, we set five different sizes for the support set: 16, 32, 64, 128, and 256. For each size, perform 10 repeated random splits of the support/query sets for the test tasks under these settings and take their averages as the final results.
Performance. Figure 3a displays the results of all compared methods. The results indicate that UniMatch outperforms all benchmarks across various support set sizes. It achieves substantial per- formance gains of 4.27%, 8.53%, 4.40%, 6.07%, and 4.26% with support set sizes of 16, 32, 64, 128, and 256, respectively. These findings highlight the effectiveness of UniMatch's dual match- ing mechanism enhancing generalization and robustness. Additionally, UniMatch demonstrates strong adaptability, consistently improving performance across different support set sizes.
Ablation Study. 1) To explore the importance of explicit hierarchical molecular matching (i.e., hi- erarchical pooling and matching) and implicit task-level matching (i.e., meta-learning) in capturing complex molecular structures, baselines by incorporating these hierarchical mechanisms and a meta-learning strategy. 2) eval- uate the transferability of UniMatch, we test it several common GNN architectures, including GIN (Xu et al., 2019), GCN (Kipf Welling, 2017), GAT (Veličković et al., 2018), and Graph- SAGE (Hamilton al., 2017). Experimental results, shown in Figure 3b, highlight the significant advantages of the dual matching mechanism effectively processing complex molecular structures and its strong adaptability and transferability across different GNNs.
several common GNNs baselines. UniMatch extends these
hub-benchhark Performance. The FS-Mol benchmark (157 test tasks) is divided into 7 sub- set tasks (Stanley et al., 2021). The results of UniMatch and baselines on these subset tasks are presented in Appendix E.2. Table E.2 shows the UniMatch outperforms state-of-the art methods.
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GSK3 (AUROC)
JNK3 (AUROC)
HIV (AUROC)
Tox21 (AUROC)
ADKF-IFT
UniMatch
XGBoost
CDDD
GCN
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Attentive FP
0:15
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PreGNN
TrimNet
ToxCast (AUROC)
PCBA (PRAUC)
MUV (PRAUC)
SVM
Meta-GAT
Figure 4: The performance of all compared methods on the seven classification tasks with support set size on the Meta-MolNet benchmark. Each colored sector represents method, with the height of the sector indicating its effectiveness. Starting from the black arrow, the methods listed in the legend in a counterclockwise direction. UniMatch corresponds to the orange sector. The dashed orange circle marks the results of UniMatch. Methods with sectors below this line do not surpass UniMatch, while those above show superior performance.
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4.3 CROSS-DOMAIN DRUG DISCOVERY ON THE META-MOLNET BENCHMARK
Benchmark and Baselines. Meta-MolNet (Lv et al., 2024) sets a standard for evaluating gen- eralization in computational chemistry by improving data quality and testing rigor. We evaluate our model on classification tasks including GSK3, JNK3, HIV, Tox21, ToxCast, PCBA, and MUV. For comparison, consider four types of baselines: 1) Classical machine learning methods: sup- port vector machine (SVM) (Bao et al., 2016), extreme gradient boosting (XGBoost) (Deng et al., 2021), and random forests (RF) (Fabris et al., 2018). Supervised learning methods: GCN (Kipf & Welling, 2016), CMPNN (Song et al., 2020), DMPNN (Yang et al., 2019), Attentive FP (Xiong al., 201 and TrimNet (Li et al., 2020). 3) Self-supervised learning methods: CDDD (Winter
et al., 2019), Mol2Context-vec (Lv et al., 2021), MolBERT (Fabian et al., 2020), N-gram (Liu et al., 2019), and Pre-GNN (Hu et al., 2020). 4) Meta-learning methods: ADKF-IFT (Chen et al., 2023) and Meta-GAT (Lv et al., 2024), All baseline results are reproduced according to Lv et al. (2024). Due to the sub-task settings Meta-MolNet, prototype-based methods are no longer appli- cable. Further details can be found in Appendix D.I.
Evaluation Procedure. To evaluate the generalization ability of UniMatch, we follow higher ra- tio of molecules/scaffolds by Lv et al. (2024). For classification tasks, we use AUROC and AUPRC as evaluation metrics. Specifically, AUROC is used to measure the performance of binary classifica- tion tasks (GSK3, JNK3, HIV, Tox21, and ToxCast), while AUPRC is more suitable for tasks with severely imbalanced distributions (PCBA, MUV). All experimental results are averaged over three independent runs with different random seeds, using support set size of 2. Additional details the evaluation metrics can be found in Appendix D.3.
Performance. Figure 4 presents comparison of different methods across the seven classification datasets in Meta-MolNet benchmark. The results indicate that UniMatch performs exceptionally well on the GSK3, JNK3, Tox21, and ToxCast datasets, while showing less well the HIV and PCBA datasets. Our method encounters significant challenges on the MUV dataset, likely due to distributional biases. Overall, UniMatch exhibits excellent generalization capabilities across most datasets for new molecular scaffolds, but struggles in specific cases, such as the MUV dataset.
4.4 VISUALIZATTION
14.layer
loye
3ol.aayer
Shilayer
Figure 5: Layer-wise visualization for NR-AhR toxicity prediction. The first row presents PCA projections of 10 molecules, distinguishing active (blue) from inactive (pink) molecules. The second row displays an internal visualization selected molecule across different layers, where color intensity indicating shifts in the model's attention as the layers deepen.
To validate the importance of hierarchical representations, we visualize molecules from the tox21 dataset for the NR-AhR toxicity prediction task, as shown in Figure 5. In the second row, we select one molecule (SMILES: captures distinct structural levels, ranging from atoms and substructures to the entire molecule. Ad- ditionally, PCA projections of these 10 molecules are used to examine the distribution of active and inactive molecules. This analysis enhances understanding of the model's 's ability to distinguish molecular structures across layers, offering insights into the role of hierarchical feature extraction and its interpretability in toxicity prediction. Further details can be found in Appendix F.
to illustrate how each GNN layer

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5
RELATED WORK
5.1 GRAPH-BASED MOLECULAR PROPERTY PREDICTION
Graph-based methods are a mainstream approach for the few-shot molecular property prediction task. PAR (Wang et al., 2021) and ADKF-IFT (Chen et al., 2023) employ GIN (Xu et al., 2019) as the molecular encoder, while Meta-MGNN (Guo et al., 2021) utilizes Pre-GIN (Hu al., 2020). Meta-GAT (Lv al., 2024) adopts GAT (Veličković et al., 2018) to learn molecular representa- tions. However, these methods typically focus on single-scale molecular features and overlook the hierarchical nature of molecular structures (Altae- Tran et al., 2017; Ren al., 2018; Zhuang al., 2023). In addition, several approaches (Zhao et al., 2023; Liu ct al., 2024a) combine the strengths of Large Language Models (LLMs) to tackle the few-shot problem, but these methods often incur high computational costs. Our method differs by incorporating molecular hierarchical structures through hierarchical pooling and matching, enabling more effective alignment of complex structures.
5.2 MATCHING LEARNING
To address the few-shot learning problem, matching learning compares new instances with small set of labeled examples to facilitate accurate predictions. Common methods include Matching Net- works (Vinyals et al., 2016), ProtoNet (Snell et al., 2017), Relation Networks (Sung et al., 2018), and LGM-Net (Li et al., 2019). While these methods perform well in Natural Language Processing (NLP) and Computer Vision (CV), they struggle with the inherent complexity of molecular graphs, which feature non- Eucliduiid structures and intricate relationships between nodes and edges. Hi- erarchical matching can mitigate this issue by capturing multi-level representations, but existing approaches still face limitations when applied to molecular data due to its unique topological com- plexity. Specifically; AMN (Mai et al., 2019) and SSF-HRNet (Zhong al., 2023), despite their improvements in feature robustness and hierarchical relationships, struggle to fully represent global structural information and generalize across complex, varied molecular graphs. Similarly, VTM (Kim et al., 2023) and HCL (Zheng et al., 2022) integrate hierarchical matching with patch-level techniques in CV, but their effectiveness diminishes when handling the structural diversity of molec- ular graphs To overcome these challenges, our UniMatch combines explicit intra-moleculas hierar- chical learning with attention mechanisms at atomic, substructural, and molecular levels, along with implicit task-level hierarchical learning via meta-learning, enhancing the model's s ability to capture saek-specific molecular information and improve generalization.
CONCLUSION
We propose Universal Matching Networks (UniMatch) to address the limitations of existing few- shot learning methods in drug discovery. UniMatch employs a dual matching framework that inte- grates explicit molecular matching with implicit task-level matching. Explicit hierarchical molecular matching provides contextual representations that support implicit task- matching, enabling bet- ter knowledge sharing across tasks. The complementary nature of these two mechanisms further en- hances model performance and adaptability new tasks. Experimental results show that UniMatch improves AUROC and A AUPRC by 2.87% and 6.52%, respectively, on the MoleculeNet, and FS- Mol benchmarks and demonstrates excellent generalization on the Meta-MolNet benchmark. Future work will focus on improving the fusion mechanism of UniMatch by adopting advanced techniques such as attention fusion or multi-scale feature aggregation to better capture the complex relationships between structural levels. Additional details and discussions are provided in Appendix H.
Acknowledgement. This work was supported by the National Science and Technology Ma- jor Project 2023ZD0120800 and the National Key Research and Development Program of China 2022YFBB4000000.
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Ethics Statement. This paper addresses few-shot molecular representation learning without in- troducing new datasets or requiring human annotation. To the best our knowledge, no additional ethical concerns arise beyond those commonly associated with research in this field.
Reproducibility Statement. We have detailed our model design in the paper, covering the archi- tecture (explicit hierarchical molecular matching in Section 3.1) and meta-learning-basee
training and inference (implicit task-level matching in Section 3.2). Further training details are provided in the Appendix. We also outline the exact implementation of UniMatch in Section and Appendix. Lastly, we will involve in the discussion regarding implementation details of our paper.
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A DETAILS OF TRAINING AND INFERENCE
A.1 ALGORITHM
Algorithm Meta- Meta-training procedure for UniMatch.
Input:
The few-shot training tasks (Trl of molecular property prediction;
Output: Trained model se:w:
1: Randomly initialize 0 and w;
2: while not converged do
3: Sample batch B of tasks [T, E B3;
4: for all {T=lT B3 do
5: Sample N and No molecules to form Suain, and Qrain,
6:
Split Susin, into S and Ø;
7:
for / = 1........ L do
(1) Obtain node representations h rig's and h of (th GNN layer by Eq. 1;
8:
9:
Obtain molecular representations z r,a 2..0" and Zr.g of 1hh GNN layer by Eq. ( 7,4' and yin of 1th GNN layer by Eq. 3;
Evaluate prediction
end for
12:
Evaluate the final prediction F7,9' and Fr.g | by Eq. 4; Evaluate training loss C (Q,, fa.w/ by Eq. 5; Fine-tune A as 0r by Eq. 6; Evaluate testing loss L Qirain Saw., by Eq. 5;
14:
16:
end for
17:
Update 0, w by Eq. 7:
18:
end while
Algorithm outlines the meta-training procedure for UniMatch, designed optimize the model for few-shot molecular property prediction tasks. The algorithm starts by initializing the parameters 0 and w and iteratively updates them through combination of inner and outer optimization steps. Within each iteration, a batch of tasks T, is sampled, and the support set Suiin, is split into S and Q₁ For each GNN layer, node and molecular representations are computed, and predictions are evaluated using the specified loss functions. The meta--paametted are fine-tuned and updated based on the performance on the query sets, ensuring effective model generalization across tasks.
A.2 DETAILS OF IMPLICIT TASK-LEVEL MATCHING
Training Phase.
Task Vector p. Expansion and Analysis. Each task Train, has its own internal task vector Pr. which can be represented as an expansion derived from the general parameter 0, the task- specific parameter w, and their relationships with other tasks. The relationship matrix M governs parameter sharing and task matching.
Using p- to Construct Task Relationship Matrix M. We want to use the task vector pr to construct the task relationship matrix M. The following methods can be used to measure the similarity or relationship between task vectors:
Dot Product Similarity:
My P..P.-
(11)
The dot product similarity measures the inner product of task vectors p, and Pj in vector space. The larger the value, the higher the similarity between the two tasks.
Cosine Similarity:
P.P.A |p-
P
M,
(12)
p
The cosine similarity measures the cosine of the angle between task vectors p, and pj. The value is in the range of [-1, 1], and the closer is to the similar the tasks are.
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Euclidean Distance:
Mr, = - -Pyl
(13)
The Euclidean distance measures the difference between two task vectors. The smaller the value, the closer the relationship between the two tasks.
Using Relationship Matrix M for Task Matching. Using the above similarity metries, can obtain a task relationship matrix M, where each element Mr.j represents the similarity or matching degree between task Tuain.r and task J. Based on this relationship matrix, introduce inter-task information sharing in the inner loop and outer loop optimization.
Inner Loop Optimization Using Task Vector w,. During the inner loop optimization. we can use the relationship matrix M to update the task-specific parameters w, of task Tiain, The inner update formula is as defined in Eq.
Outer Loop Optimization Using Task Vector Py- After W, updated, task vectors p can be further refined, which in turn can be used to update the relationship matrix M. During the outer loop optimization,
use the task relationship matrix M to update the general parameter 0, so that the model's 's performance on all tasks can be improved. The outer update formula is as defined in Eq. 10.
Relationship Matrix M Update. Next, the task vectors p are further refined, and the relationship matrix M is updated based on the differences between these task vectors.
Inference Phase. In the traditional inference phase, models typically employ explicit gradient descent strategies to update the parameters for new task. This explicit update process relies on the new task's loss value and gradient calculations. However, under the framework of implicit task matching, we aim to directly generate adaptive parameters for the new task based on its matching relationship with training tasks, thereby avoiding explicit gradient update processes.
Assuming that the current model needs to handle a new task Test.r its adaptive parameters can be represented using the following implicit matching formula:
Nis:
W
M (Wa Wj),
(14)
where Nes denotes the total number of test tasks.
Then, direct inference and prediction are performed using the fine-tuned new task parameters wy, thereby improving the sample prediction accuracy for the new task.
B DETAILS OF MOLECULENET BENCHMARK
In this section, introduce the details of datasets that included in the MoleculeNet benchmark in Section B.I. I. In addition, we show the details of the experimental setup B.2.
B. DETAILS OF DATASETS
Table Summary datasets included in MoleculeNet.
Dataset
Tox21
SIDER
MUV
ToxCast
Compounds
8,014
1,427
93,127
8,615
Tasks
12
27
17
617
Meta-Training Tasks
9
12
450
Meta-Testing Tasks
3
6
5
167
In the MoleculeNet benchmark, we perform experiments on datasets in Tabel 2, which include Tox21 (Richard et al., 2020), SIDER (Kuhn al., 2016), MUV (Rohrer & Baumann, 2009), and ToxCast (Richard et al., 2016). Widely utilized the assessment of compound toxicity for drug development and environmental risk evaluation, the Tox21 dataset, as described in Richard et al.
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(2020), contains 8,014 compounds categorized into 12 tasks. By analyzing this dataset, researchers can identify environmental pollutants and potential drug candidates, offering crucial insights into their impact human health. The SIDER dataset, introduced in Kuhn et al. (2016). serves crucial datahase of drug side effects, encompassing extensive information on medications and their associated adverse responses. This dataset encompasses 1427 compounds distributed among 27 cat- egories, Utilizing the SIDER dataset provides researchers with valuable insights into drug safety profiles and potential side effects. The MUV dataset (Rohrer & Baumann, 2009), which includes 93,127 compounds distributed among 17 tasks showcasing range of biological activities, is widely acknowledged as key standard for evaluating the multifaceted functions of drug compounds. A fundamental resource in toxicology research, the ToxCast dataset (Richard et al., 2016) is critical high-throughput screening database used to evaluate the potential health hazards posed by various compounds. With compilation of 8,615 compounds and 617 tasks, this dataset significantly con- tributes to the field of toxicology.
B.2 DETAILS EXPERIMENTAL SETUP
In HieMatch (and Pre- Matchh, GIN used in Eq. which consists of layers with hidden size 300. In addition, attention mechanism used in Eq. consist of layer with head. We implement Uni- Match in PyTorch (Paszke et al., 2019) and Pytorch Geometric library (Fey & Lenssen, 2019). We train the model for a maximum number of 5000 epoches. We employ the Adam optimizer (Kingma & Ba, 2014) with learning rate of 0.001 for meta-learning, while using higher learning rate of 0.05 for fine-tuning the matching module and fusion module within each task. The dropout rate is maintained 0.1 for all components, except for the graph-based molecular encoder. We summarize the hyperparameters used by UniMatch in Table 3.
Table Hyperparameters used by UniMatch
Hyperparameter
Explored values
Selected
learning rate for meta-learning
0.001
0.001
learning rate for fine-tuning
0.01~0.5
0.05
number of update steps for fine-tuning
1~5
5
number of layer of GNN in Eq.
5
5
number of layer of matching module in Eq.3
1
number of head of matching module in Eq.3
1
1
dropout
0.0~0.5
0.1
hidden dimension for GNN in Eq.1
300
300
C DETAILS OF FS-MOL BENCHMARK
In this section, we first introduce the details of FS-Mol benchmark (Stanley al., 2021) in Sec- tion C.I. The subsequent discussion delves into the details of the compared baselines on FS-Mol benchmark in Section C.I. In addition, further details regarding the evaluation metric AAUPRC is presented in Section C.3. Finally, the details of experimental setup on FS-Mol benchmark is presented in Section C.4.
C.I DETAILS OF BENCHMARKS
The Few-Shot Learning Dataset of Molecules (FS-Mol) (Stanley et al., 2021) is designed for ma- chine learning applications in the Quantitative Structure-Activtty Relationships (QSAR) field (Trop- sha et al., 2023), specifically focusing on few-shot learning scenarios. It comprises a total of 5120 distinct assays, encompassing 233,786 unique compounds. The dataset is partitioned into three subsets: Dtrain for training, Drest for testing, and Doatid for validation purposes. Dreat contains 157 tasks, Dtrain includes 4938 tasks, and Dvatid is composed of 40 tasks. Notably, each task in the dataset contains average of 94 compounds, notably lower figure compared to other similar datasets. This characteristic reflects the high specificity of the protein targets and the corresponding assays, posing significant challenge in the QSAR domain.
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C.2 DETAILS OF BASELINES.
In the comparative analysis of the FS-Mol benchmark (Stanley et al., 2021), four types of base- lines have been chosen: Single-task methods, Multi-task pre-training methods, Self-supervised pre- training methods, and Meta-learning methods.
Single-task Methods. The single-task methods are single-task GP with Tanimoto kernel (GP-ST) (Ralaivola et al., 2005), single-task GNN (GNN-ST) (Gilmer et al., 2017), MixHop (Abu-El-Haija et al., 2019) and CHEF (Adler et al., 2020) for context-enriched information.
GP-ST, as delineated in the study by (Ralaivola et al., 2005), encompassing the random walk kernel, Shortest-path kernel, and subtree kernel, are employed to evaluate the resemblance between graphs of chemical compounds. Gilmer et al. (2017) introduces GNN-ST, particularly focusing MPNNs for proficient learning from graph-based representations of molecules in quantum chemistry. CHEF (Adler et al., 2020) leverages fingerprint-basee based features to capture chemical information, thereby enhancing the performance of molecular property prediction tasks. MixHop (Abu-El-Hail) et al., 2019) is novel graph convolutional architecture that enables higher-order neighborhood mixing in Graph Neural Networks (GNNs). By incorporating multiple neighborhood feature mixing opera- tions, including neighborhood difference operators, the MixHop model can learn broader range of graph structural representations without increasing computational complexity.
Multi-task Pre-training Method. Multi-task GNN (GNN-MT) (Stanley et al., 2021) employs a 10-layer pre-trained GNN with 128 hidden dimensions and "principal neighborhood message aggre- gation." " Task-specific readout functions and an MLP with a 512-dimensions hidden layer produce activity label predictions. The model is fine-tuned on all tasks in Dtrain using multi-task learning.
Self-supervised Pre-training Method. The Molecule Attention Transformer (MAT) (Maziarka al., 2020) modifies the Transformer architecture (Vaswani et al., 2017) by incorporating insights on inter-atomic distances and the molecular graph structure into the self-attention mechanism.
Meta-learning Methods. Property-Aware Relation Networks (PAR) (Wang et al., 2021), Proto- typical Networks (ProtoNet) (Snell et al., 2017), GNN-MAML (Guo et al., 2021), and ADKF- IFT (Chen et al., 2023) are four typical meta-learning methods. Specifically, PAR (Wang et al., 2021), introduces a property- dings into a substructure-aware representation which relevant to the target property, and designs an adaptive relation graph learning module jointly estimate the molecular relation graph and refine the molecular embeddings with respect to the target property. Schimunek et al. (2023) proposes MHNI's approach, utilizing Modern Hopfield Network (MHN) (Ramsauer al., 2020) to link molecules with an extensive array of reference molecules, thereby enhancing the covariance struc- ture of the data and mitigating spurious correlations of molecules. ProtoNet (Snell et al., 2017), a simple approach to few-shot classification, learns an embedding where each class is represented by a prototype, computed as the mean of the embedded support examples for that class. Clas- sification is then done by computing distances from the query example to each class prototype. GNN-MAMI. (Guo al., 2021) uses graph neural networks to learn molecular representations, and employs meta- meta-learning framework for model optimization. It also incorporates molecular struc- ture, self-supervised modules, and self-attentive task weights to exploit unlabeled data and address task heterogeneity. ADKF-IFT (Chen al., 2023) combines the representational power of deep learning with the probabilistic modeling capabilities of gaussian processes, enabling efficient and uncertainty-aware molecular property prediction through meta-learning.
embedding function that transforms generic molecular embed-
C.3 EVALUATION METRICS OF FS-MoL BENCHMARK
The AAUPRC (Area Under the Curve for Frecision-Recall serves as a pivotal statistical measure utilized for assessing enhancements the efficacy of classification models when confronted with imbalanced datasets due to targeted modifications, like algorithmic adjustments data processing methodologies. By contrasting the precision- curve's 's area prior to and post adjustments, this metric adeptly elucidates the extent of enhancement in the capacity of model to identify minority classes, thereby supplying a quantitative foundation for optimizing the model and facilitating decision- makinn support.
alterations in
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In line with the research conducted by Stanley et al. (2021), employ the AAUPRC as evalua- tion metric for comparing all baseline models. The specific calculation formula is detailed below:
N1(
A AUPRC (fo.w) AUPRC (fo.w)
(15)
N
where the (1) represents the number of active molecules in query set º,.
C.4 DETAILS OF EXPERIMENTAL SETUP
In UniMatch, the hyperparameters used by UniMatch reported in Table 3. What is more, FS-Mol benchmark (Stanley et al., 2021), we set the batch task 21 and weight decay 5e-5, And we train the model for 10,000 epoches.
D DETAILS OF META-MOLNER BENCHMARK
In this section, first introduce the details of Meta-MolNe benchmark (Lv al., 2024) in Sec- tion D.I. In addition, we provide the details of the baselines in Section D.2. Finally, the details of evaluation metric is provided in Section D.3.
D.1 DETAILS BENCHMARKS
Meta-MolNet is an innovative benchmarking platform designed to improve molecular machine learning models by integrating diverse datasets through multitask and transfer learning, spanning applications from drug discovery to materials science. In this paper, we use 7 classification tasks on Meta-MolNet benchmark to evaluate our UniMatch, which include GSK3, JNK3, HIV, Tox21, Tox- Cast, PCBA and MUV. The GSK3 dataset focuses predicting the activity of compounds against the GSK3 enzyme, which is associated with diseases like diabetes and Alzheimer's. The JNK3 dataset assesses the inhibitory activity of compounds against JNK3, kinase implicated in neurode- generative diseases. The HIV dataset contains data for predicting the ability of compounds to inhibit HIV replication. Tox21 evaluates the toxicity of compounds across multiple biological pathways, while ToxCast predicts the toxic effects of environmental chemicals. The PCBA dataset measures compound activity across various bioassays from the PubChem database. Lastly, the MUV dataset provides rigorous and unbiased benchmark for validating virtual screening methods. Together, these tasks offer a comprehensive evaluation framework for molecular machine learning models. The detailed description of datasets in Table 4.
Table 4: Detailed Description the benchmark datasets
No. of Molecules
No. of Scuffolds
Molecules/ Scaffolds ratio
Task type
Datasets
Category
Data type
Tasks
Metries
Threshold
GSK3
Biophysies
SMILES
3,197
38
84.13
ROC-AUC
30
Single Task
Classification
JNK3
Biophysics
AMILES
4,873
78.60
ROC-AUC
HIV
Biophysics
SMILES
ROC-AUC
Tox21
Physiology
SMILES
12
2,119
12
176.58
ROC-AUC
30
Multi Task Classification
ToxCast
Physiology
SMILES
617
2,372
14
169.43
ROC-AUC
30
PCBA
Biophysics
SMILES
128
21,835
642.21
PRC-AUC
200
MUV
Biophysics
SMILES
17
11,671
152
76.78
PRC-AUC
30
D.2 DETAILS BASELINES
Four types of baselines classical machine learning models, graph-based models, message passing neural networks, and self-supervised pre-training models are chosen for comparative analysis on the Meta-MolNet benchmark (L.v et al., 2024).
Classical Machine Learning Methods. Support Vector Machines (SVM) (Bao et al., 2016), ex- treme gradient boosting algorithms (XGBoost) (Deng al., 2021), and Random Forests (RF) (Fab- ris et al., 2018) are among the classical machine learning methods that utilize descriptors and/or fingerprints commonly found in traditional QSPR/QSAR models (Cherkasov al., 2014). Notably,
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the Extended Connectivity Fingerprints (ECFPs) (Rogers & Hahn, 2010; Glen al., 2006) and Molecular ACCess System (MACCS) keys (Bender et al., 2004; Unterthiner et al., 2014) are widely used as fingerprints in such models. SVM (Bao et al., 2016) is a robust machine learning algo- rithm designed to identify the optimal solution for classification tasks by determining the maximum margin hyperplane within high-dimensional space. XGBoost (Deng et al., 2021) is proficient ma- chine learning technique that utilizes distributed gradient boosting to provide rapid, adaptable, and user-friendly solutions. Information about RF (Fabris al., 2018) can be found in Appendix C.I.
Supervised Learning Methods. Graph Convolutional Networks (GCN) (Duvenaud et al., 2015), Directed Message Passing Neural Networks (DMPNN) (Yang et al., 2019), Communicative Mes- sage Passing Neural Networks (CMPNN) (Song et al., 2020), Attentive FP (Xiong et al., 2019), and Triplet Message Networks (TrimNet) (Li et al., 2020) are among the supervised learning meth- ods. Specifically, GCN' (Duvenaud et al., 2015) employs convolution operations based on the eigen decomposition of the Laplacian matrix, which allows them to aggregate information from neighbor- ing nodes and derive node embedding representations. DMPNN (Yang et al., 2019) use Laplacian eigen decomposition for convolution operations, aggregating information from neighboring nodes to derive node embeddings. CMPNN' (Song et al., 2020) enhance modeling of molecular properties by using node-edge interaction module to effectively integrate atom and bond features. Atten- tive FP (Xiong et al., 2019) employs atom and bond attributes to create feature vectors, preserving spatial information and capturing both local and nonlocal effects with a graph attention mechanism. TrimNet? (Li et al., 2020) utilizes triplet message mechanism to extract edge information from atom-bond-aton interactions, achieving state-of-the-art performance.
Self-supervised Learning Methods. CDDD (Winter et al., 2019). MolZContext-vec (Lv et al., 2021), MolBERT, N-gram, and Pre-GNN (Hu et al., 2020) are self-supervvsed methods that pre- train on large molecular datasets to extract meaningful descriptors. These data-driven approaches produce generalizable features, avoiding fixed extraction rules and reducing overfitting. Specifically, CDDD (Winter et al., 2019) learns features from large chemical structure corpus by translating be- tween different molecular representations, compressing shared information into a low-dimensional vector. Mol2Context-vee" (Lv et al., 2021) uses Bi-LSTM to create dynamic representations of molecular substructures, capturing intramolecular hydrogen bonds and other non-covalent inter- actions. 'oIBERT (Fabian et al., 2020) is Transformer-based model that uses BERT (Devlin et al,, 2018) to learn high-quality molecular representations for drug discovery. N-gram !! (Liu et al., 2019) captures co-occurrence patterns of local substructures by extracting n-grams from the graph and creating a histogram to represent their frequencies, forming the graph-level representation. Pre- GNN (Hu et al., 2020) pre-trains graph neural networks by learning representations both node and graph levels, capturing local and global structural information in molecular graphs
Meta-learning Methods. Meta-GAT (Lv et al.. 2024) and ADKF-IFT (Chen al., 2023) two typical meta-learning methods. Specifically, Meta-GAT (Lv et al., 2024) s a graph attention network that uses cross-domain meta-learning to predict molecular properties with few examples. By extracting keoweeee from similar molecules across domains, reduces sample complexity and quickly adapts to new scaffold molecules with minimal data. ADKF-IFT 14 (Chen et al., 2023) can be seen in Appendix C.I.
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D.3 EVALUATION METRICS OF MOLNET
In this paper, we use benchmark datasets with higher ratio of molecules to scaffolds, presenting a significantly more challenging scenario compared to random cross-validation and datasets with lower ratio (Lv et al., 2024), for evaluating generalization ability. For classification tasks, we use Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision- Recall Curve (AUPRC) as evaluation metrics. Specifically, AUROC measures the trade-off between the true positive rate (sensitivity) and the false positive rate (1 specificity) across different classi- fication thresholds. AUROC ranges from to 1, where 0.5 represents a random classifier and represents perfect classifier. A higher AUROC value indicates better classification performance, making it well-suited for evaluating binary classification tasks such as GSK3, JNK3, HIV, Tox21, and ToxCast. Mcanwhile. AUPRC considers the trade-off between precision (positive predictive value) and recall (sensitivity). Like AUROC, AUPRC ranges from 0 to I, with higher values in- dicating better performance. AUPRC particularly useful for evaluating models on imbalanced datasets, making it more suitable for tasks such as PCBA and MUV, which have severely skewed distributions.
D.4 DETAILS EXPERIMENTAL SETUP
On the Meta-MolNet benchmark, we set the query set size to 8 and the support set size to 2. We employ the AdamW optimizer (Loshchilov & Hutter, 2017) with a learning rate of 0.001 for meta- learning and an inner learning rate of 0.001 for fine-tuning the task-specific modules within each task. weight decay of 5e-4 applied. The model is trained for 100 epochs ensure robust performance.
E FURTHER EXPERIMENTS RESULTS ON FS-MOL
E.I OVERALL PERFORMANCE
Figure 6 (a)~(e) show the performance of different methods in classifying 157 FS-Mol (Stanley et al., 2021) test tasks across various support set sizes via box plots. The box plots show the distri- bution of classification accuracies for each method, providing insight into their overall performance and effectiveness in handling varying support set sizes. Our HieMatch demonstrates superior per- formance compared to the state-of-the- (SOTA) method across all metrics.
E.2 SUB- BEBBHENC PERFORMANCE
FS-Mol (Stanley et al., 2021) divides tasks into sub-benchmarks using Enzyme Commission (EC) numbers (Hu et al., 2012), allowing for assessment across the entire benchmark. In classification tasks with a support set size of 16, Table E.2 illustrates the performance of the top methods across all aub-benchmarks. The results highlight that, while excelling in overall performance, HieMatch emerges as the top performer in half of the sub-benchmarks for classification tasks.
Table 5: The classification performance for the 16 support set size.
FS-Mol sub-benchmark (EC category)
Method
Class
Description
#tasks
GP-ST
GNN-MAML
ADKF-IFT
UniMatch
oxidoreductases
0.081 0.032
0.013 0.019
0.046
0.023
0.103 0.0036
0.231 :: 0.075
2
kinuses
125
0.082 + 0.006
0.004
0.178 = 0.009
0.247 0.010
0.256 = 0.012
3
hydrolases
20
0.158 0.026
0.062 + 0.019
0.106 + 0.024
0.213 + 0.029
0.201 + 0.028
lysases
0.218 0.172
0.161
0.218 0.147
0.223 1 0.160
0.211 0.061
5
isomerases
0.119 +: 0.029
-0.014 4 0.015
0.006 :: 0.021
0.121 + 0.049
0.087 : 0.025
ligases
0.027 0.069
-0.011
1 0.003
0.001 = 0.017
0.103 - 0.066
0.359 - 0.011
7
translocuses
0.102 0.053
0.067
1067:00000 0.050
0.021
0.082 - 0.049
-0.009 0.011
all enzymes
157
0.083 0.007
0.021 0.00S
0.162 0.009
0.230 0.009
0.245 0.011
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PAR
PAR
CNN-ST
GNN-ST
MAT
MAT
GNN-MT
CNNMT
GP-ST
SP-ST
GNN-MAMI
CNI-MAMI
CHEF
CHEF
MixHopp
MixHop
MHNS
MHNS
ADKF-IFT
ADKF-IFT
UniMacch
UniMatch
0.1
0.5
0.9
0.5
AAUPRC
(a) 16 support set size
(b) 32 support set size
PAR
GTN
MAT
GNN MT
GMN
GT-ST
GP-ST
GNN MAML
GNN MAML
Net
ProtoNet
CHEF
CHEF
MixHop
MiHog
MHNK
MHN
UniMatch
UniMatch
-0.
6.0
11.1
0.2
0.3
0.4
0.5
0.9
0.2
0.41
006
1..
AAURRC
AAUPRC
(c) 64 support set size
(d) 128 support set size
PAR
GNN-ST
MAT-
GNN-MT
GP ST
GAML
ProtoNes
CHEF
MixHop
MHNG
ADKF-IFT
UniMarch-
AAUPRC
(e) 256 support set size
Figure 6: Box plots illustrate how different methods perform in classifying 157 FS-Mol test tasks across various support set sizes.
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E.3 META-TESTING COSTS
In this section, we compare the inference time of our UniMatch with meta-learning approaches. Figure iillustrates that UniMatch takes slightly more time compared ProtoNet (Snell et al., 2017) and GNN-MAMI (Guo et al., 2021). Additionally, ADKF-IFT (Chen et al., 2023) exhibits the longest reference time. However, it is important to note that UniMatch still maintains relatively fast inference time, making it a viable option for meta-learning tasks.
120
Wall-clock time (s)
80
60
40
20
ProtoNet
ADKF-IFT
GNN-MAML
UniMatch
Figure 7: The wall-clock time, along with standard errors, is recorded during meta-testing on predetermined set FS-Mol classification tasks for comparison with the meta-learning approaches.
F VISUALIZATION EXPERIMENTS
The details of the molecules used in Section 4.4 represented in Table 6.
Table 6: The ten molecular sampled from the Tox21 dataset are used for the NR-AhR toxicity prediction task. "1" indicates active molecules, while "0" indicates inactive molecules.
SMILES
Label
CCCCиCCCCCCCC==)CCCC
O
Ne cctccc/NNNicc2cccc))eee
COCC(C)O
O
1
ONelcceeed
CCCOJCNCCCC)0
0
G RELATED WORK
G.1 HIERARCHICAL REPRESENTATION LEARNING ON GRAPHS
Hierarchical representation learning is crucial for graphs as captures multi-scale structures, en- abling models to discem both local and global patterns effectively (Grattarola et al., 2022).
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Existing methods can be divided into three categories: layer-wise hierarchical methods, architecture- level methods, and supergraph-based methods. Layer-wise hierarchical methods, such as DiffPool (Ying et al., 2018). JK-Nets (Xu et al., 2018), K Pooling (Lee et al., 2019), ASAP (Ranjan et al,, 2020), HGP-SL (Zhang et al., 2019), and MixHop (Abu-RI-Haija et al., 2019), primarily capture graph structures by clustering nodes or using adjacency matrix powers, but they struggle to balance local and global structures, limiting their ability to capture multi-level features. Addi- tionally, architecture-level methods, such as FraGAT (Zhang et al., 2021b), MGSSL (Zhang et al., 2021a), and UniCorn (Feng et al., 2024) capture multi-scale features but struggle with task-specific structural changes and often lack generalization, particularly for new tasks. Supergraph methods, like HiMol (Zang et al., 2023), offer hierarchical representation, but they are hindered by high com- putational complexity and insufficient sensitivity to structural diversity. To solve this issue, HiPM (Kang et al., 2024) introduces prompt tree to model relationships between tasks. However, this approach still requires a substantial amount of data to be effective.
H DISCUSSION, LIMITATION AND FUTURE WORK
Conclusion. In this paper, we propose Universal Matching Network (UniMatch) to address the limitations of existing few-shot learning approaches in drug discovery. UniMatch leverages dual matching mechanism that integrates hierarchical molecular matching and implicit task-level match- ing to capture multi-scale structural features and inter-task relationships effectively. By utilizing hierarchical pooling and matching techniques, UniMatch aligns representations across atomic, sub- structural, and molecular levels, preserving essential structural details that often overlooked by single-scale methods. Our experimental results demonstrated that UniMatch outperforms state-of- the-art methods the MoleculeNet and FS-Mol benchmarks, with significant improvements in AUROC and AAUPRC. Additionally, UniMatch showed exceptional generalization ability on the Meta-MoINet benchmark. However, our analysis revealed that the model's performance on regres- sion tasks could be further improved by addressing specific issues in the fusion module.
Limitation: Simple Fusion Design. The fusion mechanism in the proposed UniMatch model is relatively simplistic, which may limit its ability to effectively integrate information from different hicrarchical levels. This design could result in suboptimal performance, as the model may not fully capture complex interactions and dependencies across multiple scales of molecular structures. More sophisticated fusion strategies, such as attention- could enhance the model's capability to combine features more effectively. Leveraging such ad- vanced techniques would enable UniMatch to exploit the rich hierarchical information inherent in molecular structures, thereby improving prediction accuracy and generalization. Although these advanced fusion methods may increase computational complexity, the potential gains in model per- formance justify this investment.
fusion or multi-scale feature aggregation,
Limitation: Underfitting on Regression Tasks. Our UniMatch model exhibits underfitting on regression tasks, indicating that it may not be capturing all the necessary features and complexi- ties required for accurate predictions. Experimental results show that the underfitting issue arises from using a linear layer in the final fusion module. When different layer features are combined using a weighted average approach instead, the model achieves significantly better performance and converges properly. This suggests that the linear layer may not adequately capture the relation- ships between features from different layers for regression tasks. Therefore, replacing the linear fusion with a weighted average aggregation method could resolve the underfitting issue and allow the model to better capture complex feature relationships, thereby improving its performance on regression tasks.
Future Work. the future, we will focus on enhancing the fusion mechanism within UniMatch to better capture the complex relationships between features from different hierarchical levels. Specif- ically, we will explore advanced fusion techniques such as attention-based fusion and multi-scale feature aggregation to replace the current simplistic linear approach. Additionally, we plan to con- duct more extensive experiments on a wider range of datasets and tasks to ensure the robustness and generalizability of our model. Another promising direction is integrate domain-specing edge and features into the model to further improve its predictive accuracy. As part of our ongo- ing efforts, we also plan further investigate the interpretability of UniMatch by incorporating
knowl-
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Published as a conference paper at ICLR 2025
gradient-based methods (e.g., Deep (Shrikumar et al., 2017)) and exploring the Kolmogorov- Arnold Network (KAN) (Liu et al., 2024b) to gain deeper insights into feature importance and model decision-making. Finally, will work optimizing the computational efficiency and scalability of UniMatch to facilitate its application in large-scale drug discovery projects.
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Reviews:
[{"review_id": "k3I6WEcrsr", "reviewer": "Reviewer_XF6i", "summary": "The authors propose UniMatch, a dual matching framework that integrates hierarchical molecular matching with meta-learning, significantly improves the performance and generalization in molecular property prediction tasks.", "questions": "1. Do AUCPR and AUPRC refer to the same metric? If so, I sugget using a consistent abbreviation throughout the paper.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.  UniMatch is the first method that integrates multiple levels of molecular structures, demonstrating outstanding performance across various tasks and sigificantly contributes to the filed of drug discovery.\n2. This paper is clearly-written and accompanied supported by well-designed figures, well done!", "weaknesses": "1. I suggest further investigating the interpretability of the model, particularly with respect to the multi-level representations. Some gradient-based methods (e.g. DeepLIFT) could be used to reveal the importance of these features.", "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": "fbz6pMRwYO", "reviewer": "Reviewer_PgJj", "summary": "The paper presents UniMatch, a framework designed to address data scarcity in few-shot drug discovery. It uses a dual matching approach: explicit hierarchical molecular matching, which captures information from the atomic level to full molecular structures, and implicit task-level matching via meta-learning to enable effective generalization across tasks.", "questions": "see weaknesses", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The concept of modeling molecular structures across multiple levels is promising.\n2. The manuscript is well-written and easy to read.\n3. Extensive experiments across diverse datasets demonstrate that the model consistently outperforms baselines.", "weaknesses": "1. The related work section is somewhat lengthy. It might be more effective to move it to the end of the manuscript or condense it.\n2. While the rationale for modeling hierarchical structures is solid, I have concerns about the use of mean pooling. This approach may lead to a predominantly molecule-level representation, potentially losing crucial atomic and substructural details. What do the authors think?", "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": "OFILOGxMNQ", "reviewer": "Reviewer_6vhN", "summary": "This study introduces a new framework for molecular representation specially aimed at multi-task few-shot learning. In this context for each task there is a small support subset of molecules with known labels ($S_T$) and a query subset ($Q_T$) with unknown labels.\n\nThe contributions of the study can be broken down into the following components:\n\n1. Matching at different levels of the molecular graph. This is achieved by an attention mechanism where the keys are the molecular features of the $S_T$, the molecular representations of $Q_T$ are used as query, and as value, the labels of the $S_T$. This matching attention mechanism is not novel. The novelty resides on repeating this matching with the representations at each layer of the GNN (thus effectively capturing information at different structural levels of any given molecule) and concatenating the resulting values that are then passed through an MLP to predict the final target value.\n2. Meta-learning. An additional implicit matching mechanism is used to learn the similarity between different tasks and how to leverage the multi-scale representations from one task to another. This is achieved by an inner loop where the task-specific adaption parameters are updated using the matrix that captures the similarity between tasks and an outer loop where this similarity matrix is updated.\n\nThe results obtained in benchmark datasets clearly show that the benefits of this approach when compared to current state-of-the-art alternatives. The ablation study also clearly supports the contribution of all the components of the method.", "questions": "1. When evaluating against MoleculeNet tasks, is there a reason why the QM tasks were not considered and only the biophysical properties are included?\n2. Do you have any ideas as to whether more explicitly introducing an inductive bias to the different levels of attention could be helpful?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "1. The paper tackles a valuable domain-specific problem, which high societal value, with a method that can be reasonably adapted to different domains.\n2. The evaluation is extensive and clearly demonstrates the advantages of the method.\n3. The ablation study is complete and clearly demonstrates the contribution of every component in the method.", "weaknesses": "Major weaknesses\n---\n1. The technical description of the dual matching mechanism is hard to follow and it is a disservice to an otherwise solid work.\n\n\nMinor weaknesses\n---\n_(These are minor points that do not have a direct bearing in my evaluation of the paper, but I think would improve its quality)_\n\n1. In line 075-077 and Figure 1, one of the examples provided of the importance of the multi-level representation of molecules from atomic, to whole molecule serve their purpose as an illustration, but it is not chemically correct. Hydrogen ions do not exist within the molecule, they would be referred to as hydrogen atoms. Though it is true that Hydrogens ions in a solution determine its acidity. In a molecule, the hydrogen atoms are not the determinants of acidity, but rather the atoms they are attached to. So in the first examples, the acidity of those molecules is not determined by the Hydrogen atoms but by the chlorine and oxygen atoms; and their electronegativity (or ability to hold onto the Hydrogen). Still the illustration is accurate, but the atom that should be highlighted is not the Hydrogen. In the case of the right example, it is arguable that the acidity is not only caused by the oxygen, but rather the whole substructure of H-O-S.\n2. In line 409, when describing the FS-Mol dataset, it is described as \"[a benchmark] for macromolecules (i.e., proteins)\". This is not accurate, the benchmark measures the ability of small molecules (the one that serve as input for this system) to bind to proteins. In other words, the tasks are whether the molecules can bind to specific proteins. This is an important distinction because due to their size, modelling proteins with the approach presented in this paper would be computationally infeasible.", "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": "tU743O2rZz", "reviewer": "Reviewer_E3W3", "summary": "This work presents UniMatch for molecular property prediction in the few-shot learning scenario. It combines explicit hierarchical molecular matching with implicit task-level matching via meta-learning. The effectiveness of the few-shot learning and multi-task generalization is validated on the MoleculeNet, FS-Mol, and Meta-MolNet benchmarks. An ablation study and visualizations are provided to demonstrate the importance of hierarchical molecular matching and task-level matching.", "questions": "1. **Generalization to different molecular structures**:\nIt would be beneficial to understand how well the method generalizes to diverse molecular structures, as this is crucial for practical applications. For example, testing under a scaffold split for support and query sets could provide insights. The paper mentions the method's failure on MUV. Is this due to a lack of structure generalization? The phrase \"severe distribution imbalance\" is not clearly explained. What does it refer to specifically? And why are other baselines able to overcome this issue?\n\n2. In Figure 2, the query molecule appears identical to one in the support set. In your implementation, can the query molecule overlap with the support set? \n\n3. The numbers in parentheses following the datasets in Table 1, which appear to represent the number of tasks, do not seem to be explained in the main text.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The writing is clear, with well-crafted figures that enhance understanding. It also offers a discussion of limitations and detailed supplementary explanations.\n\n2. The experiments are quite extensive. The research examines the universality of the method across different network structures. It also conducts evaluations on three benchmarks. The results are convincing.", "weaknesses": "1. **Timeliness**:\n - The baselines and related work employed in the paper lack more recent works, especially those of recent 1 or 2 years. \n\n- The pretraining model, Pre-GNN (Hu et al., 2020), is relatively old in the context of molecular pretraining. Recent years have seen the emergence of far more powerful models. I encourage authors to consider integrating more up-to-date pretraining models to showcase the significance and practical usage of their approach. \n\nBy simple searching, I find more recent related works on Hierarchical molecular representation learning methods like \"UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning\" (ICML 2024) and \"Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction\" (Briefings in Bioinformatics, 2024).\n\n\n\n2. **Novelty**:\n\nThe hierarchical molecular representation extraction concept is commonly seen in the literature. Additionally, the paper employs standard attention and meta-learning methods. I'm concerned that this may not meet the novelty standards of top conferences.", "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 (Spotlight)