Distilling and Adapting:  A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks
Authors: Alana Deng
Sugitha Janarthanan
Yan Sun
Zihao Jing
Pingzhao Hu
Venue: ICLR 2026 Poster (2026)
Keywords: Graph representation learning
contrastive learning
multiplex networks
knowledge distillation
zero-shot prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information.  To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher–student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Published as a conference paper ICLR 2026
DISTILLING AND ADAPTING: A TOPOLOGY-AWARE FRAME- WORK FOR ZERO-SHOT INTERACTION PREDICTION IN MUL- TIPLEX BIOLOGICAL NETWORKS
Alana Deng', Sugitha Janarthanan", Yan Sun', Zihao Jing', Pingzhao Hu" Hu,. Department of Computer Science, Western University, London, ON, Canada 2 Department of Biochemistry, Western University, London, ON, Canada
ABSTRACT
Multiplex Biological Networks (MBNs), which represent multiple interaction types be- tween entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-awan graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework out- performs state-of-the-arr methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.
1 INTRODUCTION
Exploring complex interactions within biological networks is vital for advancing personalized medicine and drug discovery. These interactions enable targeted therapies and the study of disease pathways, calling for predictive models that are both precise and reliable. Moving beyond traditional single-layer analyses, multiplex networks provide powerful framework to capture the multi-dimensional nature of biological systems, with each layer representing distinct interaction type, providing holistic picture of biological connectivity across diverse contexts.
Current predictive models (Kipf & Welling 2016 Hamilton et al. 2017 Zhu al. 2018 Rao et al. 2022
Tao et al. 2023 Dang Vul (024). often rely on single-layer network analyses. Their limitations can be summarized in three areas: (1) Handling Multiplex Data: Single- representations discard relational heterogeneity and semantic distinctions between interaction types, overlooking context-dependent reasoning where interactions may rely on both relation type and broader network context. (2) Integrating Multimodal Information: These models have limited capacity to combine biological or chemical sequence features with network topology, which essential for capturing complex biological interactions. (3) Zero-Shot Prediction: They struggle to predict interactions for unseen entities without prior neighborhood data, restricting generalization to novel biochemical entities.
*Corresponding author. Departments of Computer Science and Biochemistry, Western University, 1400 Western Road, London, Ontario N6G 2V4, Canada. E-mail: phu49 uwo.ca (P.H.)..

---

Published as a conference paper ICLR 2026
To address these shortcomings, propose CAZI-MBN (Context-Aware and Zero-shot Interaction prediction in Multiplex Biological Networks), a framework tailored for multiplex biological interaction prediction. Its four key contributions include: (1) Multiplex-oriented representation learning, capturing interaction-type-speciii context-aware patterns across layered networks. (2) Unified graph-sequence learning, leveraging domain-specific LLMs for biochemically informed representations. (3) Strategic knowledge distillation, transferring latent knowledge from observed to unobserved regions to enable zero- shot prediction. (4) A Mixture of Experts (MoE), adaptively modeling diverse interaction types and label dependencies for multi-label prediction.
2 RELATED WORK
Domain-Specific LLMs. Pre-trained language models have become useful tools for biological sequence analysis by learning patterns from large corpora of protein, molecular, or genomic sequences. ProtTrans (Elnaggar et al. (021)) and ESM-2 (Lin et al. 2023) enhance predictions of protein function and structure, while ChemBERTa (Chithrananda et al. 2020 trained on tens of millions of SMILES strings (Kim et 2023), captures chemical syntax and substructure patterns. DNABERT (Ji et al., 2021) and DNABERT-2 (Zhou et al. 2023) extend these approaches to genomic motifs and regulatory elements.
Interaction Prediction and Graph Representation Learning. Interaction prediction in networks has advanced through graph representation learning, which embeds nodes or subgraphs into vector spaces preserving topology. Random-wall methods (Perozzi al. 2014 Ribeiro al. 2017 Dong et al.) [2017 Gao
ett al. 2018: Grover & Leskovec 20166 gave way to Graph Neural Networks (GNNs) (Scarselli et al. 2008
Wu al.] (2200) Zhang & Chen 2018; Xia et al., 2024) Kim & Oh, 2022), including GCN (Kipl & Welling
2016), Graph Transformer (Shi et al. 2020), and GraphSAGE (Hamilton et al., 2017) for neighborhood aggregation. Contrastive approaches like DGI (Veličković et al., 2018) further improve representations by maximizing mutual information. Relation-aware GNNs (Schlichtkrull et al.) 2018 Chen et al.) 2021) also expand these capabilities by parameterizing message passing with respect to edge types, enabling the model to learn relation-specifi transformations and aggregate signals that vary across interaction modes.
Multiplex Network Modeling. Multiplex networks can be modeled as Knowledge Graphs (KGs), which
enable semantic reasoning (Walsh et al. 2020 Mohamed et al. [202]] Chen al.) [023] Chandak et al. 2023)
but flatten layered structures and lose interaction-type specificity. limiting zero-shot performance. Multi-layer network models (Park al. 2020 Jing et al., 202 Gallo al.
2023 Gu al., 2024 instead preserve intra- and inter-layer dependencies, supporting relation-awan aware learning and multiplex link prediction. Yet, in MBNs (Xiang al.) 2021 Yu al. 2022 Valdeolivas et al. 2019), integrating sequence features and capturing complex biomedical dependencies across layers remains an open challenge.
Knowledge Distillation. Knowledge distillation (Hinton 2015) trains a smaller "student" to emulate a larger "teacher," enabling efficient deployment with minimal performance loss. It has proven effective in computer vision (Chen et al., 2017) and NLP (Sanh 2019). However, its potential to MBNs and zero-shot generalization remains largely unexplored.
To summarize, existing approaches lack a systematic framework that unifies sequence embeddings with multiplex structures. Single-layer methods overlook relational heterogeneity. multiplex models underuse multimodal features, and most approaches rely on neighborhood data, with little effort devoted to exploring zero-shot generalization.
2

---

Published as conference paper ICLR 2026
3 PROBLEM STATEMENT
Definition: Multiplex Networks. A multiplex network is graph 9 = (V, Bintra] = (. .r.) where V set of nodes across layers, Pis is a set of entities, and Eintra represents the set of intra-layer edges, Each layer G (P.. Vi, E, represents the graph of an interaction type in G, where P., V,, and E; are respectively the entity set, node set, and edge set for layer i, for i 1, 1.....
multiplex network, entity (e.g. gene, compound, protein) can be present as nodes in multiple layers, while node sets and the connectivity (edges) can differ from one layer to another. That is, Pi...., , PL E P are intersecting while V and Ei...., Er E E are disjoint. Each node E Vi corresponds an entity P. This
Vz Vi...., allows the representation of different types of relationships or interactions between the same set of entities. An inter-layer graph Ginter (V, Enter) can be formed by connecting the same entities in different layers g.
Supra-Adjacene Matrix. Given multiplex network G, there is set of adjacency matrices A {A1, A2,
A...... ... A where A, E R Vi is the layer-specific adjacency matrix for layer i. A encodes the intra-layer links in G. Ginter can be represented by inter-layer adjacency matrix C E R. Vix[V), The supra-adjacency matrix, which encodes both intra-layer and inter-layer connections, is defined as
A C,C,
(1)
:E(L.....L)
where + denotes the direct sum. The illustration of an example multiplex network and its supra-adjacency matrix can be found in Figure [
Maltpliss G
Adjornary
Inne-dayer Adjaemny Matrier
Supee-Aljeccne M B,A +
0
0
Rdstiosship Tope
0
0
0
Ag
Figure 1: Illustration of an example multiplex network and its supra-adjacency matrix. A1 and Ag are the layer-specific adjacency matrices for layer and 2, C is the inter-layer adjacency matrix, and Å is the supra-adjacency matrix that represents the example multiplex network G.
Generalizing Interaction Prediction in Multiplex Networks: From Seen to Unseen Entities. Interaction prediction multiplex networks multi-label classification task, since entity pairs can share multiple interaction types. Transductive settings predict missing links among seen entities, while zero-shot settings require predicting interactions for entirely unseen entities with no prior known neighborhood.
4
METHODS
The proposed CAZI-MBN framework addresses multiplex biological interaction prediction in both transduc- tive and zero-shot settings. integrates domain-specific LLM embeddings (see Supplementary Section A) with topelogy-aware network representations. A Context-Awark Enhancement (CAE) module (see|Sup plementary Section C) captures inter-layer dependencies, while a MoE model enables multi-label prediction and knowledge distillation supports zero-shot inference. The workflow is shown in Figure
3

---

Published as conference paper ICLR 2026
Training of the teacher model in CAZI-MBN is guided by hybrid loss function:
L = Ldiec + Freg + Lats + 3||@1,
(2)
where Liise and Lreg are the discriminator's loss and the consensus regularizer's loss from the CAE, Cots is a multi-label soft margin loss (see /Supplementary Section B and | is the coefficient for 12 regularization applied to the trainable parameters θ The student model is trained with the sum of a Mean Square Error (MSE) distillation loss Cdistill and Lele.
Giveal Muttiles Ricegical Netwark (MINN)
Teacher-liely Knentedgy (Tapalog))
Ghee: mraisigg MIN
trained bour and student medee
Stared Krunslteigy (Seepances)
Netwark: Tapelagy
Transdacttee
Compoaada 2
Genen
Unitidd Takeatur (LSGT)
Priar rebodiing avallable
ChembERla2
DNABERTT-2
18H--
Sequenee
Prolktin
Prier arkraciions kovnna
Contenst-Aaare
Minuua Expert (Mal))
Laker Repeossnaligg
Doiillatien
Subascunent (CAL)
Direct cuslirendal integration (Segaance Topologs) Cusset handle staice entities withoon pe interatioons
Frier EGT Lnlddddee seoroliaate Neegannc Infermatiinn avallable
Stecket Modl
Zere-She
Misture of Experr (Mall)
Repemettttin
Sad
Toet Palr
Tesk
direet matitmodal integration. Lat's laam from A
(2) Overall Training Workdoow CAZL (KD)
(b) Infereace in Two DicTecem Scttings: Trunsductive and Zare-Shaa Shot
Figure 2: (a) The overall training workflow CAZI-MBN. (b) Two inference settings CAZI-MBN: Transductive and zero-shot.
4.1 FEATURE REPRESENTATION
To represent the multimodal nature of MBN data, our approach integrates complementary sources of information: sequence-basee embeddings derived from domain-specific pre-trained LLMs and topology- based representations generated by Unified Graph Tokenizer (UGT).
4.1.1 SEQUENCE EMBEDDING: DOMAIN-SPECIFIC LLMS
We use three pre-trained LLMs for sequence embeddings: ChemBERTa (Chithrananda et al. 2020) for drug/metabolite SMILES, DNABERT-2 Zhou al. 2023) for gene sequences, and ESM-2 (Lin et al. (023) for proteins.
4.1.2 UNIFIED GRAPH TOKENIZER (UGT)
The UGT generates generates topology, multiplexity, and high-order connectivity-aware node embedding from the supra-adjacency matrix Á by (1) constructing a smoothed high-order matrix À = A + A ..... ........... d
with A = 1-1/2 AD-1/2 and (2) projecting copoooyy-awar embeddings. For node v E V:
e,- A... LN(UVE VVE), e, E Rdd.
(3)
where U, Ve E x and Σ Rdd xd, are from the SVD of Á.
4

---

Published as conference paper ICLR 2026
4.2 CONTEXT-AWARE ENHANCEMENT (CAB)
The CAE module refines multiplex embeddings through node- and layer-level inter-layer attention combined with contrastive learning (Figure B and (Supplementary Section C). Each layer is encoded with a Graph Transformer, and inter-layer attention enables nodes adaptively attend counterparts across interaction types. A contrastive learning framework aligns embeddings by training a discriminator to distinguish real from perturbed edges, while consensus regularizer learns shared embedding that maximizes agreement across true layers and minimizes alignment with negative views. This design enhances generalization in complex biological systems by improving representation consistency and contextual relevance across the multiplex structure. A CAE forward pass is outlined in Algorithm
Algorithm I CAE Forward Pass
Input: Feature matrices X,, graphs Gi, size L
for i 1 to L do
G - NegSampling(G) (Negative sampling]
H; - rans former (X,, G:) (Feature matrix X;
H Hatt: + NodeConteat
Attention
(Node-level context-aware inter-layer attention)
H; + GraphT rans former (X,, G:)
Hedge; + Get Edge F Feature (H
G2) (Edge embedding]
Hedge + Get Edge eature (H,, G:) (Negative edge embedding]
Si + (AvgPoo ool Hedge.)
logitdise. disc. + Discriminaton (Hedge;) Hedgers 8i)
Caise; + BCF discy logit,isc. end for
H + ATTENTION (Hott ..... Hatt_L)
H < ATTENTION
H.) (Layer-level attention aggregation]
disn + SU (Caise, Ediser.) (Discriminator's s loss)
Z + Consenssuseeullarieer Regularizer(H. H) (Updated embeddings)
Lreg + 1 + CosineSim H, Z) CosineSim( H, Z) |Regularizer's loss]
Orggeel
Graph Woeks
Reakest Blocks
Negatte Newwer
6
Preittne Frabedding
Diserinatutte
Tranfarteer
Blick,
AngPook
Layer
Naded and Contest
Resularlect
Origial
Entecling
Agun
Negito
Tranformer Block,
Diagge
Laper
Figure 3: Illustration of the CAE module.
4.2.1 NODE-LEVEL CONTEXT- INTER-LAYER ATTENTION
Node-level context-aware inter-layer attention enables CAZI-MBN adaptively weight entity's represen- tations across layers. The weight on (p++) quantifies how much node n in layer p attends to its counterpart in layer q.
5

---

Published as conference paper ICLR 2026
σ (BMM) (H!,)) @ H!!))
(p+4) on
softmax
(4)
14 21=1i4pp (Dlv) (HH)) H
where ((P) learnable weight vector, and of.) iis a sigmoid activation function.
4.2.2 LAYER-LEVEL CONTEXT-AWAN CONSENSUS EMBEDDING
capture the varying importance of interaction types, CAZI-MBN applies an attention module that aggregates layer-specific node features into and its negative counterpart H. context-aware contrastive learning carning-based based consensus regularizer then learns consensus embedding Z E RlPIxd by maximizing agreement with H/ and minimizing disagreement with H, using cosine-similarity loss.
4.3 MIXTURE OF EXPERTS (MoE)
In MBNs, interaction prediction is multi-label classification task since entity pairs can share multiple interaction types [ To handle label sparsity and interdependence, we use a MoE framework where each expert captures distinct interaction patterns, and gating network assigns weights based the input. For embedding h, experts (h)) 1xt produce logits, with weights softmax bg); the final
prediction is Y = k 1K f=
4.4 KNOWLEDGE DISTILLATION
We apply knowledge distillation in CAZI-MBN to enable zero-shot interaction prediction (Figure 2). The teacher leverages sequence and topology embeddings but depends on neighborhood context, while the student is topology-agnostic and relies only sequence data. By minimizing the MSE between their latent representations, the student approximates the teacher's 's richer features, enabling effective zero-shot inference.
5
EXPERIMENTS
5.1 EXPERIMENT SETUP
5.1.1 DATASETS
our knowledge, no standardized benehmark datasets currently exist for evaluating models in the context MBNs. To address this gap and provide reliable, well-validated benchmarks, curated five MBNs from
high-quality data sources (Cannon et al. 2024; Zdrazil et al. 2024] Li et al. 2024 Peng et al.) 2025 Han
et al. 2018) published in high-profile journals. A summary of the MBNs provided in Table with details on data collection and preprocessing available Supplementary Section D.
5.1.2 BASELINES AND EVALUATION
We benchmark CAZI-MBN against 11 models across four categories: sequence-based (Chen & Guestrin
2016), single-graph (Kipf & Welling 2016 Hamilton et al. 2017 Shi et al. 2020; Veličković al.
2018), multiplex grraph-based/relttion
(Boccaletti et al. 2014: : Park et al.
[2220] Jing ct al. 2021
Schlichtkrull et al. 2018 Chen et al. 2021). and domain-specific RRao et al. 2022 Tao et al. 2023
Chen
jtt al. 2019 Dang & Vu 2024: Bai et al. 2023 Huang et al.) 2021] et al. 2022
Fakhry et al.
2023
(Table 2). Since most not designed for multiplex networks, train separate classifier per interaction
6

---

Published as conference paper ICLR 2026
Table I: Dataset summary (ITs: Interaction Types)
Context
# of Nodes
IT's
# of IT's
DGIdb
Drug-Gem
1846
Interaction mechanisms
ChEMBL
Compound-Bactent
9368
Response phenotypes
PINNACLE
Protein-Proteit
7044
Interaction different cell types
12
MetaConserve TRRUST
329
Conservation in bacteria strains
4
Gene-Gene
2,862
Gene regulartory roles
type. For zero-shot comparison, we also include knowledge-distilled variants of models lacking native zero-shot capability.
Table 2: Benchmark models
Type Sequence-based
Benchmark Models
MultiLayer Perceptron (MLP), XGBoost (XGB)
Single graph-based based
GCN GraphSAGE, Graph Transformer, DGI
Multiplex graph-based/Relationn
MultipeeSAAGE, DMGI, HDMI, R-GCN, R-GAT
(1) Drug-Gene & Compoud-Ban -Botur-aaatera:: CoSMIG, DGCL
(2) Protein-Protein: PIPR, xCAPTS
Domain-Specine
(3) Protein-Metabolite: DrugBAN, MolTrans
(4) Gene-Gene: DL-GGI, GENER
We evaluate performance with Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and multi-label metrics including Hamming Score (HS) and Subset Accuracy (SA). Each experiment is conducted over three repeated trials, and report the mean and Standard Deviation (SD) of each metric. For details of the evaluation metrics and experimental settings, see Supplementary Section E. and Supplementary Section E.2
5.2 EMPIRICAL ANALYSIS
We conduct a comprehensive evaluation of CAZI-MBN on five benchmark multiplex biological networks under both transductive (T) and zero-shot (ZS) settings. These two evaluation modes capture different forms of generalization: the transductive setting evaluates how well the model interpolates within partially observed network, while the zero-shot setting tests its ability to infer interactions for nodes that are not seen during training.
Across the full set of 13 baselines, Tables 3 and domain-specific models for each dataset, with comparisons based on AUROC. These tables show circumstances where domain-oriented approaches are competitive and situations where multiplex methods benefit from access to multiple interaction types.
and present the strongest single-graph, multiplex,
Detailed results for all benchmark models, including AUROC, AUPRC, HS, and SA in both evaluation settings, are included in (Supplementary Section E.3.I A focused analysis of performance on minority interaction types, which typically have fewer observations and more irregular training signals, is provided in [Supplementary Section E.3.2 This analysis helps assess the stability the models in label-imbalanced conditions that commonly arise in biological networks.
7

---

Published as conference paper ICLR 2026
Figure reports the average performance drop when each module CAZI-MBN (LLMs, UGT, CAE, MoE) is removed. This summary highlights the contribution of each component in both transductive and zero-shot settings. Expanded ablation results and dataset-level discussions appear Supplementary Section E.3.3 Figure reports module-wise performance drops for each dataset, and the error bars indicate variability across interaction types.
Additionally, we include case study on IBD-related genes in Supplementary Section E.3.4, which illustrates how CAZI-MBN prioritizes biologically meaningful interactions real disease context. The parameter counts of the teacher and student models presented in SSupllementary Section E.3.5 to document the computational footprint of each component and to clarify the efficiency gains of the student architecture. To provide insight into how different interaction types contribute to the multiplex representation, the layer-specific attention weights from CAE are reported in (Supplementary Section E.3.7
Table 3: Evaluation of transductive and zero-shot multiplex interaction prediction on DGIdb (top model per category).
Setring Model
AUROC
AUPRC
HS
SA
Graph Transformer
0.505=00007
0.514+0.005
0.493+1.0004
0.508++0.003
HDMI
0.551=00.006
0.557:10.007
0.540:10.012
0.511
T
DGCL
0.519100005
0.531=10.004
0.527+10.007
0.512:10.006
CAZI-MBN
0.715
0.729:100009
0.687+10011
0.684+0.011
Graph Transformer
0.498
0.502:10.004
0.486+1.004
0.50410.007
DMGI
0.524
0.528+0.015
0.529
0.502++0.006
7S
DGCL
0.513+00.003
0.515
0.509+00011
0.502++0.004
CAZI-MBN
0.671+-0.008
0.709-+0.011
0.688 00000
0.663++0.01
*See Table in Supplementary Section E.3. for full results of all evaluated models.
Table 4: Evaluation of transductive and zero-shot multiplex interaction prediction ChEMBL
(top model per category).
Setting
Model
AUROC
AUPRC
HS
SA
DGI
0.651:0.019
0.71940.005
0.738::.012 0,67310.011
HDMI
0.663
0.762+0.014
0.789+0.012
0.730++0.006
CoSMIG
0.661
0.732+0.011
0.755++0.009
0.702+00025
CAZI-MBN
0.012
0.006
0.889
0.757+00001
Graph Transformer
0.62810.011
0.720+10008
0.72510.013
0.628
DMGI
0.652+0.021
0.745
0.756+0.007
0.711#0.009
ZS
CoSMIG
0.641+0.011
0.733+0.007
0.729+0.010
0.683+1.000
CAZI-MBN
0.791:1.0118
0.839
0.857+0.011
0.723+10.009
*Sce Table
Supplementary Section E.3. for full results all evaluated models.
We note the following key observations: (1) CAZI-MBN consistently outperforms all benchmarks in AUROC and AUPRC, with improvements of 3.1-20.4% across the five multiplex networks in both transductive and zero-shot settings. These results indicate that the model is able to generalize across multiple biological contexts. (2) Flexible calibration strategies help close HS and SA gaps. For datasets where CAZI-MBN lags on HS/SA, our post-hoc per-label calibration experiment (Supplementary Section E.3.6) shows that performance improves noticeably with more flexible thresholds, suggesting the benefits of
8

---

Published as conference paper ICLR 2026
Table 5: Evaluation of transductive and zero-shot multiplex interaction prediction on PINNACLE (top model per category).
Setting
Model
AUROC
AUPRC
HS
SA
Graph Transformer
0.701
0.704
0.700:10.005
0.726:10.024
HDMI
0.773=0.022
0.798
0.776#0.017
0.766=10.016
T
XCAPTS
0.781
0.804#0.014
0.726+0.012
0.752+0.015
CAZI-MBN
0.001
0.845:0.011
0.75111.009
0.772
Graph Transformer
0.68710.019
0.69010.017
0.680+0.018
0.705+0.019
HDMI
0.74
0.77640.0021
0.75710.024
0.742400014
ZS
XCAPTS
0.785=0.012
0.791
0.733+0.0013
0.739:10.016
CAZI-MBN
0.81210.013
0.820-1.0088
0.748
0.76311.011
*See Table in Supplementary Section E.3. for full results of all evaluated models.
Table 6: Evaluation of transductive and zero-shot multiplex interaction prediction on MetaConserve (top
model per category).
Setting
Model
AUROC
AUPRC
HS
SA
DGI
0.66810.020
0.67310.019
0.66510.010
0.66910.010
HDMI
0.015
0.729+0.021
0.715+00006
0.718++0005
DrugBAN
0.737+0.010
0.714
0.710+000019
0.711+000008
CAZI-MBN
0.052
0.744
0.779-10.008
0.6561100014
Graph Transformer
0.651=0.0077
0.660+0.020
0.652+0.015
0..4810.015
HDMI
0.709
0.71040.014
0.704+0.014
0.707
ZS
DrugBAN
0.692+0.017
0.705
0.707+0.015
0.708+10011
CAZI-MBN
0.738
0.722:10.110
0.749-0.020
0.653=00.009
*See Table 1
Supplementary Section E.3.
for full results of all evaluated models.
Table Evaluation of transductive and zero-shot multiplex interaction prediction on TRRUST (top model
per category).
Setting
Model
AUROC
AUPRC
HS
SA
DGI
0.841+0.014
0.844+0.011
0.845+00.099
0,847+0.006
HDMI
0.878+0.008
0.879=0.014
0.883+40.005
0.881+40.004
T
GENER
0.867+00012
0.868=0.006
0.865+00.100
0.869-000006
CAZI-MBN
0.905
0.872=0.008
0.79111.023
0.784+0.015
DGI
0.832+0.011
0.835=0.014
0.836+0.007
0.838+0.006
HDMI
0.868+0.006
0.869
0,872+0.006
0.870=00.004
ZS
GENER
0.857+00.007
0.858
0.855+00.006
0.859-L0.007
CAZI-MBN
0.89910.017
0.775-1.0019 0.869-0.013
0.764+0.007
*See Table 14]in
Supplementary Section H.3.
1 for full results of all evaluated models.
9

---

Published as conference paper ICLR 2026
Transductive
Zero-Shot
0.16
AUROC
AUPRC
0.14
HS
Average Porfrmance Drop
SA
0.12
0.10
0.08
0.06
0.04
0.02
0,00
wi UGT
we LLMe
wio CAE
wo MoE
wio UGT
woo LLMs
wlo CAE
wio MaE
Figure 4: Average metric-wise performance drop across all five datasets when each module is ablated.
per-label calibration strategy, as interaction types often have different score distributions. (3) Multiplex and domain-specific models such as DMGI, HDMI, CoSMIG, xCAPT5, and GENER usually outperform single-graph baselines, underscoring the importance of incorporating relation-specifer structure and biological context. (4) The interaction type-specific analysis (Supplementary Section E.3.2) shows that CAZI-MBN performs well on minority interaction types in zero-Shot settings, suggesting that the model remains stable even when training data for certain interaction types are scarce. (5) Ablation studies (Supplementary /Section E.3.3 and Figure 4h show that all components make contributions performance. LLMs contribute the largest gains (15-20% AUROC), followed by CAE (7-10%) and UGT/MoB (5-8%). These results suggest that the overall design relies on several complementary sources of information. (6) The case study (Table 18 in SSpplemmentary Section E.3.4) shows that CAZI-MBN recovers a substantial proportion of known interactions (82.7% DGIdb, 85.7% TRRUST, 75.1% 1% PINNACLE, 62.5% MetaConserve) and identifies literature-supported IBD-related links such as GAPDIH-Omigapi (Foley et al. 2024 Zhou et al. 2015 Özsoy
et al. 2022 These results demonstrates the model's 's biologically coherent predictions in a zero-sho shot setting
6 CONCLUSION, LIMITATIONS, AND FUTURE WORK
We present CAZI-MBN, a zero-shot framework for interaction prediction in multiplex biological networks that combines domain-specifie I.Mss, multiplex-awar embeddings, contrastive attention, and teacher-student knowledge distillation, achieving strong performance and robust generalization to unseen entities. To our knowledge, this is the first framework specifically designed for zero-shot multiplex interaction prediction. We also curate five high-quality MBN datasets to address the lack of standardized benchmarks. CAZI-MBN shows promise for accelerating drug discovery and antibiotic research by enabling interaction prediction for novel biochemical entities, and its flexible design is applicable to other biomedical domains.
Nonetheless, the framework has limitations. In particular, it does not yet incorporate structural data (c.g.. genomic, protein, or compound 3D information), which constrains fine-grained biological modeling. Future work will integrate such structural information and extend the framework to more complex settings such as cross-species networks.
10

---

Published as a conference paper ICLR 2026
ETHICS STATEMENT
This work introduces framework for predicting interactions in multiplex biological networks, with potential accelerate discovery areas such as drug development and precision medicine. We identify no direct ethical risks and are committed to responsible dissemination, supporting the ethical use of biological data and Al technologies.
REPRODUCIBILITY STATEMENT
The source code for CAZI-MBN is publicly available at htttpp:://iituub..ommm CA2I - MBN, Details of dataset preprocessing are provided in Supplementary Section C, while the ex-
perimental protocol and evaluation setup are described in (Supplementary Section E.1 and Supplementary
Section E.2
ACKNOWLEDGEMENT
This work was supported part by the Canada Research Chairs Tier II Program (CRC-2021-00482), the Canadian Institutes of Health Research (PLL 185683, PJT 190272, PJT204042, CFA 2050559,, the Natural Sciences, Engineering Research Council of Canada NGGPIN-201--00022ALLLA Canada Foundation for Innovation (CFI) John Evans Leaders Fund (JELF) program (#43481).
602759-24) and The
REFERENCES
F Algieri, Al Rodriguez-Nogales, Jose Garrido-Mesa, Deseada Camuesco, Teresa Vezza, Natividad Garrido- Mesa, Pilar Utrilla, María Elena Cadriguez-Cabezas, Ivo Pischel, and Julio Galvez. Intestinal anti- inflammatory activity of calcium pyruvate in the unbs model of rat colitis: Comparison with ethyl pyruvate. Biochemical Pharmacology, 103:53-63, 2016.
Peizhen Bai, Filip Miljković, Bino John, and Haiping Lu. Interpretable bilinear attention network with domain adaptation improves drug-target prediction. Nature Machine Intelligence, 5(2):126-136, 2023.
Robert D Barber, Dan W Harmer, Robert A Coleman, and Brian Clark. Gapdh as housekeeping gene: analysis of gapdh mrna expression in a panel of 72 human tissues. Physiological genomics, 21(3):389-395, 2005.
Anna Bas, Göte Forsberg. Sten Hammarström, and M-L Hammarström. Utility of the housekeeping genes 18s mma, 3-actin and glyceraldehyde-3 phosphate-dehydree reverse canscriptase-polymeena chain reaction analysis of gene expression in human t lymphocytes. Scandinavian journal of immunology, 59(6):566-577 2004.
for normalization in real-time quantitative
Stefano Boccaletti, Ginestra Bianconi, Regino Criado, Charo I Del Genio, Jesús Gómez-Gardenes, Miguel Romance, Irene Sendina-Nadal. Zhen Wang, and Massimiliano Zanin. The structure and dynamics of multilayer networks. Physics reports, 544(1):1-122, 2014.
Simon Bos and Debby Laukens. Metabolic modulation during intestinal fibrosis. Journal of Digestive
Diseases, 1196):3 2020.
Matthew Cannon, James Stevenson, Kathryn Stahl, Rohit Basu, Adam Coffman, Susanna Kiwala, Joshua F McMichael, Kori Kuzma, Dorian Morrissey, Kelsy Cotto, al. Dgidb 5.0: rebuilding the drug-gent gene interaction database for precision medicine and drug discovery platforms. Nucleic acids research, 52(D1): D1227-D1235, 2024.
11

---

Published as a conference paper ICLR 2026
Seow-Neng Chan, Eden Ngah Den Low, Raja Affendi Raja Ali, and Norfilza Mohd Mokhtar. Delineating inflammatory bowel disease through transcriptomic studies: current review of progress and evidence. Intestinal Research, 16(3):374, 2018.
Payal Chandak, Kexin Huang, and Marinka Zitnik. Building a knowledge graph enable precision medicine. Scientific Data, 10,1):67,,02
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker. Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems, 30, 2017.
Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Skoda, and Valentina Tamma. Knowledge graphs for the life sciences: Recent developments, challenges and opportunities. arXiv preprint 1rXiv:2.:09117255, 2023.
Meiqi Chen, Yuan Zhang, Xiaoyu Kou, Yuntao Li, and Yan Zhang. r-gat: Relational graph attention network for multi-relational graphs. arXiv preprint arXiv:2109.05922 2021.
Muhao Chen, Chelsea J-T Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai- Chang, Carlo Zaniolo, and Wei Wang. Multifaceted protein-protein interaction prediction based on siamese residual renn. Bioinformatics, 35(14):1305-i3) 1314, 2019.
Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, 2016.
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. Chemberta: large-scale scale eeff-super pretraining for molecular property prediction. arXiv preprint arXiv:2010.0986 2020.
Tianyu Cui, Khaoula El Mekkaoui, Jaakko Reinvall, Aki S Havulinna, Pekka Marttinen, and Samuel Kaski. Gene- Gene-gene gene interaction detection with deep learning. Communications Biology, 5(1):1238, 2022.
Thanh Hai Dang and Tien Anh Vu. xcapt5: protein-protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model. BMC bioinformaties, 25 (1):106, 2024.
Anindhya Sundar Das, Anandita Basu, and Rupak Mukhopadhyay. Ribosomal proteins: the missing piece in the inflammation puzzle? Molecular and Cellular Biochemistry, pp. 1-13, 2024.
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 144-144, 2017.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al. Prottrans: Toward understanding the language of life through self-supervised learning. IEEE transactions on pattern analysis and machine intelligence, 71110)77112-1177, 2021.
Ahmed Fakhry, Raneem Khafagy, and Adriaan-Alexander Ludl. Gener: A parallel layer deep learning network to detect gene-gene interactions from gene expression data. arXiv preprint arXiv:2310.03611, 2023.
Elena Ferrer-Picón, Isabella Dotti, Ana M Corraliza, Aida Mayorgas, Miriam Esteller, José Carlos Perales, Elena Ricart, Maria C Masamunt, Anna Carrasco, Eva Tristán, al. Intestinal inflammation modulates the epithelial response to butyrate in patients with inflammatory bowel disease. Inflammatory bowel diseases, 26(1 :1:3:33335 2020.
12

---

Published as a conference paper ICLR 2026
A Reghan Foley, Pomi Yun, Meganne Leach, Sarah Neuhaus, Gilberto Averion, Ying Hu, Leslie Hayes, Sandra Donkervoort, Minal S Jain, Melissa Waite, et al. Phase 1 open-label study of omigapil in patients with lama2-or col6-related dystrophy. Neurology: Geneties, 10())e200148, 2024.
Luca Gallo, Vito Latora, and Alfredo Pulvirenti. Multiplexsage: multiplex embedding algorithm for inter-layer link prediction. IEEE Transactions on Neural Networks and Learning Systems, 2023.
Ming Gao, Leihui Chen, Xiangnan Hc, and Aoying Zhou. Bine: Bipartite network embedding. In The 41st international ACM SIGIR conference research & development in information retrieval, pp. 713-724, 2018.
Alain P Gobert, Yulan Cheng. Mahmood Akhtar, Benjamin D Mersey, Darren R Blumberg, Raymond K Cross, Rupesh Chaturvedi, Cinthia B Drachenberg. Jean-Luc Boucher, Amy Hacker, et al. Protective role of arginase in mouse model of colitis. The Journal of Immunology, 173(3)2109-22117 2004.
Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 855-864, 2016.
Weiwei Gu, Linbi Lv, Gang Lu, and Ruiqi Li. Pay attention to weak ties: A heterogeneous multiplex representation learning framework for link prediction. arXiv preprint arXiv:2406. / /904, 2024.
Thomas Guerbette, Gaëlle Boudry, and Annaïg Lan. Mitochondrial function in intestinal epithelium home- ostasis and modulation in diet-induced obesity. Molecular metabolism, 63:101546, 2022.
Will Hamilton, Zhitao Ying, and Jure Leskovee. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
Heonjong Han, Jae-Won Cho, Sangyoung Lee, Ayoung Yun, Hyojin Kim, Dasom Bae, Sunmo Yang, Chan Yeong Kim, Muyoung Lee, Funbeen Kim, et al. Trrust v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic acids research, 46(DI):D380 D386, 2018
Geoffrey Hinton. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02533 2015.
Kexin Huang. Cao Xiao, Lucas M Glass, and Jimeng Sun. Moltrans: molecular interaction transformer for target interaction prediction. Bioinformatics, 37(6):830 836, 2021.
Yanrong Ji, Zhihan Zhou, Han Liu, and Ramana Davuluri. Dnabert: pre-trained bidirectional encoder
representations from transformers model for dna-language in genome. Bioinformatics, 37(15):2112-2120 2021.
Yun Ji, Ying Yang. Shiqiang Sun, Zhaolai Dai, Fazheng Ren, and Zhenlong Wu. Insights into diet-associated oxidative pathomechanisms in inflammatory bowel disease and protective effects of functional amino acids. Nutrition Reviews, 81(1):95-113 2023.
Jie Jia, Abul Arif, Belinda Willard, Jonathan D Smith, Dennis J Stuehr, Stanley Hazen, and Paul Fox. Protection of extraribosomal rpl13a by gapdh and dysregulation by s-nitrosylation. Molecular cell, 47(4); 656-663, 2012.
Baoyu Jing, Chanyoung Park, and Hanghang Tong. Hdmi: High-order deep multiplex infomax. In Proceedings the
the Web Conference 2021, pp. 2414 2424, 2021.
Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv preprint arXiv:2204.04877 2022.
13

---

Published as a conference paper ICLR 2026
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin Shoemaker, Paul A Thiessen, Bo Yu, et al. Pubchem 2023 update. Nucleic acids research, 51(D1): D1373-D1380, 2023.
Sup Kim, June- Young Lee, Seul Gi Shin, Jin Kyung Kim, Prashanta Silwal, Young Jac Kim, Na-Ri Shin, Pil Soo Kim, Minho Won, Sang-Hee Lee, et al. Esrra (estrogen related receptor alpha) is critical regulator of intestinal homeostasis through activation of autophagic flux via gut microbiota. Autophagy, 17(10): 2021.
Thomas Kipf and Max Welling. Semi semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:/609.022 2016.
Annaīg Lan, Thomas Guerbette, Mircille Andriamihaja, Benjamin Magnin, Martin Bordet, Pierre-Jean Ferron, Agnès Burel, Roselyne Viel, Bernard Fromenty, Anne Corlu, et al. Mitochondrial remodeling and energy metabolism adaptations in colonic crypts during spontaneous epithelial repair after colitis induction in mice. Free Radical Biology and Medicine, 205:224-233 2023.
Michelle M Li, Yepeng Huang. Marissa Sumathipala, Man Qing Liang, Alberto Valdeolivas, Ashwin N Ananthakrishhan, Katherine Liao, Daniel Marbach, and Marinka Zitnik. Contextual models for single-cell protein biology. Nature Methods, 21(8):1546-1 -55-1557765177- 2024.
Yilong Lin, Wanna Xiong, Simeng Xiao, Fang Li, Zhuo Lu, Jiongyi Yan, Xuewan Fang, Xiaojuan Cui, Yilei Wen, Jianqin Liang, et al. Pharmacoproteomics reveals the mechanism of chinese dragon's blood in regulating the rsk/tse2//mor/ribosoomee pathway in alleviation of dss-induced acute ulcerative colitis. Journal of ethnooparrmacoooyy 263: 13221, 2020.
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al. Exolutionary-scat prediction of atomic-level protein structure with a 2023.
language model. Science, 7796637))1123-1111
Sameh K Mohamed, Aayah Nounu, and Vít Nováček. Biological applications of knowledge graph embedding models. Briefings in bioinformatics, 22(2):1679-1693, 2021.
Mihriban Özsoy, Nathalie Stummer, Franz A Zimmermann, René G Feichtinger, Wolfgang Sperl, Daniel Weghuber, and Anna M Schneider. Role of energy metabolism and mitochondrial function inflammatory bowel disease. Inflammatory Bowel Diseases, 28(9): 14:3-144000 2022.
Chanyoung Park, Donghyun Kim, Jiawei Han, and Hwanjo Yu. Unsupervised attributed multiplex network embedding. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 5371-5378, 2020.
Hui Peng, Sergei Kotelnikov, Megan Egbert, Shany Ofaim, Grant C Stevens, Sadhna Phanse, Tatiana Saccon, Mikhail Ignatov, Shubham Dutta, Zoe Istace, et al. Ligand interaction landscape of transcription factors and essential enzymes e. coli. Cell, 2025.
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledgee discovery and data mining, pp. 701-710, 2014.
Jiahua Rao, Shuangjia Zheng, Sijie Mai, and Yuedong Yang. Communicative subgraph representation learning for multi-relational inductive drug-gene interaction prediction. arXiv preprint (rXXi:2205.05957 2022.
Eva Rath, Antonio Moschetta, and Dirk Haller. Mitochondrial function caatiion-gatkeeeperr of intestinal epithelial cell homeostasis. Nature reviews Gastroenterology & hepatology, 15(8):497-516, 2018.
14

---

Published as a conference paper ICLR 2026
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel Figueiredo. struc2vec: Learning node representations from structural identity. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 185-394, 2017.
David A Sallman and Alan List. The central role of inflammatory signaling in the pathogenesis of myelodys- plastic syndromes. Blood, The Journal of the American Society of Hematology, 133(10):1039-1048, 2019.
Sanh. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.011 108, 2019.
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1);61-80, 2008.
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks, In European semantic web conference, pp. 593-607. Springer, 2018.
Wei-Yun Sheng and nuclear gapdh in activated lymphocytes. PLoS One, 4(7):e6322, 2009.
V Wang. Protcomic analysis of the differential protein expression reveals
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03850 2020.
Sarah A Smith, Sayaka A Ogawa, Lillian Chau, Kelly A Whelan, Kathryn E Hamilton, Jie Chen, Lu Tan, Eric Z Chen, Sue Keilbaugh, Franz Fogt. et al. Mitochondrial dysfunction in inflammatory bowel disease alters intestinal epithelial metabolism of hepatic acylcarnitines. The Journal of clinical investigation, 131 (1), 2021.
Qiyun Tang, Qianqian Ji, Weiwei Xia, Liren Li, Jian' an Bai, Runzhou Ni, and Yongwei Qin. Pyruvate kinase m2 regulates apoptosis of intestinal epithelial cells in crohn's 's disease. Digestive Diseases and Sciences, 60: 393 404, 2015.
Wen Tao, Yuansheng Liu, Xuan Lin, Bosheng Song, and Xiangxiang Zeng. Prediction of multi-relational drug gene interaction via dynamic hypergraph contrastive learning. Briefings in Bioinformatics, 24(6): bbad371, 2023.
Alberto Valdeolivas, Laurent Tichit, Claire Navarro, Sophie Perrin, Gaelle Odelin, Nicolas Levy, Pierre Cau, Elisabeth Remy, and Anaïs Baudot. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics, 35(3):497-500 2019.
Petar Veličković, William Fedus, William Hamilton, Pietro Liò, Yoshua Bengio, and Devon Hjelm. Deep graph infomax. arXiv preprint arXiv: 1809. 10341, 2018.
Brian Walsh, Sameh K Mohamed, and Vit Nováček. Biokg: A knowledge graph for relational learning on biological data. In Proceedings of the 29th ACM International Conference on Information & &nnowldgge Management, pp. 3173-31 118,0 2020.
Gaoying Wang, Jintao Yuan, Ji Luo, Dickson Kofi Wiredu Ocansey, Xu Zhang, Hui Qian, Wenrong Xu, and Fei Mao. Emerging role of protein modification in inflammatory bowel disease. Journal of Zhejiang University-SCIENCE B, 133 2022.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. JEEE transactions on neural networks and learning systems, 32(():4 ):4-24 24, 2020.
15

---

Published as conference paper ICLR 2026
Lianghao Xia, Ben Kao, and Chao Huang. Opengraph: Towards open graph foundation models. arXiv preprint arXiv:2403.01 2024.
Ju Xiang, Jiashuai Zhang, Ruiqing Zheng, Xingyi Li, and Min Li. Nidm: network impulsive dynamics on multiplex biological network for discase-gene prediction. Briefings in Bioinformatics, 22(5):bbab080,
2021.
Liang Yu, Mingfei Xia, and Qi An. A network embedding framework based integrating multiplex network for drug combination prediction. Briefings in bioinformaties, 23(1):bbab336) 2022.
Barbara Zdrazil, Eloy Felix, Fiona Hunter, Emma J Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij. Harris Ioannidis, David Mendez Lopez, Juan F Mosquera, et al. The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic acids research, 2024.
52(D1):D1180-DD
Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. Advances in neural information processing systems, 31, 2018.
Xiang Zhou, Wen-Juan Liao, Jun-Ming Liao, Peng Liao, and Hua Lu. Ribosomal proteins: functions beyond the ribosome. Journal of molecular cell biology, 7(2):92-104, 2015.
Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana Davuluri, and Han Liu. Dnabert-2: Efficient foundation model and benchmark for multi-species genome. arXiv preprint arXiv:2306. /5006, 2023.
Siyi Zhu, Jiaxin Bing, Xiaoping Min, Chen Lin, and Xiangxiang Zeng. Prediction of drug-gent interaction by using metapath2vec. Frontiers in genetics, 9:248, 2018
Tao Zou and Jiyan Zhang. Diverse and pivotal roles of neddylation in metabolism and immunity. The FEBS Journal, 288(13)33884-391, 2021.
16

---

Published as conference paper ICLR 2026
DISTILLING AND ADAPTING: A TOPOLOGY-AWARE FRAMEWORK FOR ZERO-SHOT INTERACTION PREDICTION IN MULTIPLEX BIOLOGICAL NETWORKS
Domain-Specifie Pre-trained LLMs
17
B Multi-Label Soft Margin Loss (MLSML)
18
The CAE Module
18
Datasets and Preprocessing
19
D.I DGIdb
19
D.2 ChEMBL
19
D.3 PINNACLE
21
D.4 MetaConserve
21
D.5 TRRUST
22
Experiments and Evaluation
22
Evaluation Metrics
22
Experimental Settings.
23
E.3
Supplemental Experimental Results
24
................................
E.3.1
Empirical Analysis
................................
24
E.3.2
Interaction Type- Specifcc Analy Analysis.
30
E33
Ablation Studies
31
E.3.4
4
Case Study: Interactions Involving IBD- Related Genes and Proteins
35
Parameter Counts and GPU Time
37
E.3.6
TTRUST: Post-Hoc Peb-Label Calibration
37
E.3.7
Layer-Specific Attention Weights
37
A DOMAIN-SPECIHIC PRE-TRAINES LLMS
DNABERT-2 (Zhou et al. 2023). DNABERT-2 is domain-adapted language model designed for nucleotide sequences, extending the capabilities of the original DNABERT by incorporating deeper architecture and pretraining on a larger corpus of genomic data. It tokenizes DNA sequences us- ing k-mers and leverages the transformer architecture to capture regulatory patterns and sequence de- pendencies across long genomic regions. DNABERT-2 has demonstrated improved performance on range of genomics tasks such as promoter recognition, splice site prediction, and enhancer identifi- cation. In our work, we use the public checkpoint zhihan199 39/p//ABER---D-D--- 2- 7M (available at https://hugingface http:/hhaan11///aaaaT----22-1112
17

---

Published as a conference paper ICLR 2026
ChemBERTa (Chithrananda et al. 2020). ChemBERTa is a transformer-based language model pre- trained on SMILES strings representing chemical structures. By treating SMILES as specialized lan- guage, ChemBERTa facilitates downstream tasks such as molecular property prediction, reaction classifi- cation, and drug-likeness assessment. Its embeddings serve as chemically informed representations that integrate both syntactic and structural aspects of small molecules. In our work, we use the public check- point
ChemBBRTa - aartaa-7M---MM --7-MMM (available at ChemBERTa- 777MMMMM
ESM-2 (Lin al., 2023). ESM-2 is protein language model developed by Meta AI, pre-trained on hundreds of millions of protein sequences using a masked language modeling objective. It models the grammar of amino acid sequences and captures biologically relevant features such as structural motifs, binding sites, and evolutionary conservation without explicit supervision. Compared to its predecessor, ESM-2 offers deeper transformer layers and larger-scale training, enabling superior performance across protein structure prediction, variant effect analysis, and protein-proteen interaction tasks. Its embeddings encode rich evolutionary and functional signals useful for downstream applications proteomics and systems biology. In our work, we use the public checkpoint facebook /esm2 t. 15 DMM_RCCD (available at https://hugging:
co/ www.book/eeo/eeeeeo//eo/ooa.oo// esm2 100m__300____ 150M UR50D)
MULTI-LABEL. SOFT MARGIN Loss (MLSML)
The MLSML is defined as:
Cale
Mij log(o(z<))) + (1 Wiy) looglll111 (xii)),
(5)
where N is the number of samples, No is the number of classes, Vij is a binary indicator for the presence of class j in sample i, Zij is the raw model output for class j of sample i, and ar is the sigmoid function.
C THE CAE MODULE
For each multiplex layer G, Graph Transformer encodes node features X, into embeddings H,. Negative sampling generates perturbed graphs G2 and corresponding embeddings H;. Context-aware node-level inter- layer attention refines H; into Hau, from which edge features are extracted. A discriminator differentiates real edges from corrupted ones. Embeddings are then aggregated across layers with attention, and consensus regularizer produces updated embeddings Z.
The CAE module starts with layer-specific node embedding. For each multiplex layer Gi(i {1...., L}) in the multiplex network, Graph Transformer module first updates the layer-specific node embeddings according to:
H H((+) =a (LN (MHA (I+T)+ + +n!") n,!")..!").
H7!") E I denotes the feature matrix of multiplex layer G; at Graph Transformer layer 1, wr(") the layer-specific weight matrix, MHA performs multi-head self-attention over the node features, and d represents the output dimension. To generate the layer-specific negative node embedding Hi, negative sampling is applied to G and the same Graph Transformer module defined in Equation processes the negative network G
where LN represents layer normalization,
The edge feature matrix Hedge; IR. Ed and the negative edge feature matrix Hedge; E IR x2d are constructed by concatenating the node features of adjacent nodes within G; and G,, respectively. A high-level
18

---

Published as conference paper ICLR 2026
graph summary vector s; is computed to encapsulate the layer-level edge information for each G2 using average pooling:
[[[AAvgool(HHage))) (8: E IR22d)
(7)
where is the logistic sigmoid nonlinearity.
The speciisp BCE loss Laise, can be calculated using the edge embedding matrix Hodge, its summary representation 8;, and its corresponding negative matrix Hatge;
Liiss; Σ
log D ((Hedge, io 86)
Σ log(1 'D (eteee,, 8:))
(8)
e
eyE
where 3 specifies the feature vector for edge ej, É, represents the edge set of G, and D dis- criminator function that evaluates patch-summary representation pairs. The discriminator ' is defined as:
((Hedgo 8:) o @HHed, M,8;)
(9)
where signifies the logistic sigmoid function, and M, E 2dx2d 2d is trainable scoring matrix.
D DATASETS AND PREPROCESSING
Table 8
shows the summary statistics of five MBNs.
D.1 DGIDB
The Drug Drug-Gene Interaction Database (DGIdb) (Cannon et al. 2024 aggregates comprehensive information on drug-gene interactions and gene druggability from diverse curated sources.
The multiplex nature of DGIdb reflects the biological complexity wherein single drug gene pair may exhibit multiple modes of interaction. For example, a drug may function as an agonist under certain conditions, but act as an inhibitor, modulator, activator in others. These variations can arise due factors such as dosage, cellular context, or the presence of cofactors.
During preprocessing, entries lacking interaction type annotations were removed, and semantically similar interaction types were merged to reduce redundancy.To emphasize higher-confidence associations, we excluded interactions with scores falling in the lowest 15% quantile. We remove drugs that appear less than 7 times and genes that appear less than 4 times. Furthermore, to address class imbalance, the four least prevalent interaction categories were combined into single class, simplifying downstream modeling and evaluation. We retained five broad mechanism categories after preprocessing: agonist, activator, modulator, inhibitor/blocker, and other.
D.2 CHEMBL
ChEMBL (Zdrazil 2024) is manually curated database of bioactive molecules with drug- -ike properties. In ChEMBL, different Minimum Inhibitory Concentration (MIC) values for the same compound-bactedd pair indicate that the bacterium may exhibit varying levels of susceptibility to compound under different experimental conditions at different times. A compound bacteria pair with multiple MIC values might show different response phenotype Susceptible (S), Intermediate (I), or Resistant (R), based on environmental or genetic factors. This variability inform clinicians of potential shifts in bacterial resistance over time, the need for adjusted drug dosages, or the importance of considering specific responses when choosing treatment strategies.
19

---

Published as conference paper ICLR 2026
Table Summary Statistics of Datasets.
Dataset
Interaction Types
Nodes
# Edgees
Interaction Types
Multiplex Rate (%)
Inhibitor (7,004)
Modulator (1,439)
DGIdb
Agonist (915)
1,846
8,443
1.34
Activator (104)
Other (170)
(120,265)
ChEMBL
(33,925)
9,368
248,582
3
14.45
R (94.392)
Enterocyte-LI (4,089)
Enteracyle-S1 (7,433)
Goblet-LI (3,073)
Goblet-ST (6,258)
Paneth-III (8,291)
Paneth-S1 (15,093)
PINNACLE
7,044
101,443
12
23.26
CD4-Helper (6,893)
404-Memory (7.190)
CD8-Cytotoxic (8,172)
Treg (15,626)
B (9,261)
Memory-B (10,064)
Campylobacteraals (109)
Desulfovibrionales (98)
MetaConserve
329
377
4
27.10
Fusobacteriales (94)
Veillonellales (76)
Activation (3,149)
TRRUST
Repression (1,922)
2,862
9,396
8.9
Unknown (4.325)
# node pairs that appear 2 2 layers
Multiplex Rate
x 100%.
# unique node pairs the multiplex graph
Our benchmark a filtered, higher-quality subset of ChEMBL. We retained only assays with MIC-related measurements that could be converted into unified units, kept entries with valid canonical SMILES to ensure consistent compound representation, and applied a hinimum-frequent bacterial strains that appear more than 1l times. These steps reduce noise and sparsity, yielding coherent dataset suitable for reliable multiplex interaction prediction rather than reflecting the full size the ChEMBL database.
filter by keeping only compounds and
We preprocess ChEMBL into a multiplex network by (1) standardizing the standard units to "ug.mL-1"; (2) defining cutoffss for S, I, and R:
Susceptible (S): MIC < 4;
Intermediate (I): MIC < 16;
Resistant (R): MIC 16.
20

---

Published as a conference paper ICLR 2026
D.3 PINNACLE
We curated the Protein Network-based Algorithm for Contextual Learning (PINNACLE) dataset from et al., 2024). The original dataset captures context-aware protein-prote interactions across 156 cell type contexts spanning 62 tissues, derived from the Tabula Sapiens single-cell transcriptomic atlas. Protein-protein interactions can differ across cell types because protein expression levels, post-translational modifications, subcellular localizations, and interacting partners vary depending on the cellular environment. A protein may be active in one cell type but absent or inactive in another, or it may participate in different pathways depending the cell's functional role. These context-dependent variations critical for understanding cell-specific mechanisms of health and disease.
For study, we selected 12 cell types specifically associated with Inflammatory Bowel Disease (IBD) construct a cell type-specific multiplex mooeein-protein interaction network. This enables us to investigate cell-yype-dependent protein interactions relevant IBD pathogenesis and therapeutic targeting, capturing the cellular heterogeneity of intestinal inflammation.
We selected the following cell types based on their established roles IBD:
Enterocytes (Enterocyte of epithelium of large intestine and Enterocyte of epithelium of small intestine): These epithelial cells form the intestinal barrier and regulate nutrient absorption and immune signaling, both which are disrupted in IBD.
Goblet cells (Small intestine goblet cell and Large intestine goblet celly: Goblet cells produce mucins that maintain the protective mucus layer; their depletion is linked to barrier dysfunction and increased inflammation in IBD.
Paneth cells (Paneth cell of epithelium of small intestine and Paneth cell of epithelium of large intestine): These cells secrete antimicrobial peptides and are often dysregulated or dysfunctional in inflamed or chronically affected IBD tissue.
CD4+ helper T cells (CD4-positive helper cell): Key mediators of adaptive immunity and cytokine signaling, CD4 T cells are known to drive pathogenic responses in IBD.
CD8 cytotoxic T cells (CD8-positivee alpha-beta cytotoxic T cell): These cells contribute to epithelial damage by directly killing intestinal epithelial cells in active IBD.
Regulatory T cells (Tregs) (Regulatory T cell): Tregs suppress immune responses and maintain tolerance; impaired Treg function is frequently observed IBD patients.
Memory T cells (CD4-positive ulpha-beta memory T cell): These cells retain antigen- memory and are implicated in the persistent immune activation characteristic of chronic IBD.
B cells (B cell): B cells contribute to antigen presentation and cytokine production and can influence both protective and pathogenic pathways IBD.
Memory B cells (Memory cell): These cells involved in long-term humoral responses and are elevated in -BD-associated lymphoid aggregates and inflamed mucosa.
D.4 METACONSERVE
We curated a cell type-specific protein-mann metabolite MBN dataset, which we named MetaConserve, from (Peng et al. 2025), which systematically mapped high-confidence ligand interactions in Escherichia coli using affinity purification mass spectrometry and structural modeling. This dataset provides a valuable foundation for exploring metaholite-mediated regulatory mechanisms at the host-microbe interface.
To investigate the evolutionary conservation of these -rotein- interactions in the context of IBD, we selected four representative bacterial orders: Campylobacteralle, Desulfovibrionalls, Fusobacteriales, and
21

---

Published as a conference paper ICLR 2026
Veillonetlales, all of which have been implicated in IBD pathogenesis. Campylobacterates (e.g., Campylobac- ter concisus) are frequently enriched in inflamed mucosa and are associated with epithelial barrier disruption and proinflammatory signaling. Desuffovibrionale are sulfate-reducing bacteria that produce hydrogen sulfide, genotoxic metabolite elevated in IBD patients and implicated in mucosal injury. Fusobacteriales, particularly Fusobucterium nucleatum, are linked to mucosal inflammation and have been shown to exacerbate disease through immune modulation and epithelial adhesion. Veillonellales are enriched in dysbiotic gut microbiota and are associated with IBD disease activity and disrupted bile acid metabolism.
We leveraged conservation scores provided in the original dataset to identify pootein-metabolee that are preserved across these four orders, retaining only those with a conservation score 2 to ensure high-confidence evolutionary retention. These conserved interactions define four distinct interaction types in our multiplex network, enabling a strain-aware investigation of microbial metabolic activity relevant to IBD onset and progression.
interactions
D.5 TRRUST
The Transcriptional Regulatory Relationships Unraveled by Sentence- Text mining (TRRUST) (Han ct al. 2018) dataset is curated database of human and mouse transcriptional regulatory interactions. It compiles experimentally validated regulatory relationships between transcription factors (TFs) and their target genes, extracted through literature mining and manual curation. Each entry in TRRUST specifies the regulator (TF), the target gene, the mode of regulation (activation or repression), and the supporting evidence. TRRUST is widely used in systems biology and regulatory network analysis, offering high-confidence resource for studying gene regulatory mechanisms, constructing transcriptional networks, and evaluating computational predictions of Tarter interactions.
In our experiments, we use the human transcriptional regulatory data from TRRUST. As a gene can exert multiple regulatory effects on another gene, such as activation, repression, an undefined role, and the resulting network is modeled as multiplex graph. This structure captures the complexity of transcriptional regulation, where interactions may not be limited to single regulatory type and can reflect more nuanced or context-dependent mechanisms.
EXPERIMENTS AND EVALUATION
E. EVALUATION METRICS
Let there be N samples and No possible labels. We denote the ground-truth labels for sample i by a binary vector y; (JA.1, 24. ... :V..N. E {0, 1)Ne, and the predicted labels by ÿ: -Wr,1, 21.2..... Ne, Below, we briefly define several common evaluation metrics.
Area Under the ROC Curve (AUROC). For each label j. we can compute the Receiver Operating Characteristic (ROC) curve by plotting the True Positive Rate (TPR) versus the False Positive Rate (FPR) at various threshold settings. The area under this curve for label is denoted AUROC,. The AUROC in multi-label classification is calculated by macro-averagee
No AUROC,
AUROC- NG
(10)
Area Under the Precision-Recall Curve (AUPRC). For each label j. the Precision-Recal curve constructed by plotting Precision versus Recall different thresholds. The area under this curve for label j is
22

---

Published as conference paper ICLR 2026
AUPRC,. The macro-average of AUPRC for multi-label classification is:
No
AUPRC-
Nc Σ AUPRC,
(11)
Hamming Score (HS). The HS measures the fraction of correctly predicted labels across all samples and all labels. Often, the Hamming Loss (HL) is defined first:
N N
ΣΣ
( + 0t.))
(12)
N No i=1j=1
where 1-) is the indicator function, which is 1 if the condition is true and 0 otherwise. The Hamming Score is then
N Ne
HS HL= N No
1 11i * Dr.s).
(13)
Subset Accuracy (SA). SA, also known as Exact Match Ratio, measures the fraction of samples for which all labels match exactly. Formally,
1 Eyyy=
SA
(14)
N
Here, (y: = (c) is and only if every label is predicted correctly for sample i, and 0 otherwise.
E.2 EXPERIMENTAL SETTINGS.
We divide our dataset into training, validation, and test sets with proportions of 75%, 15%, and 15%, respectively. Each experiment is repeated three times, and we report the mean and SD of each evaluation metric. In multiplex networks, negative sampling is defined over node pairs rather than individual layers. We treat a node pair (v, v) as a positive instance if it appears in at least one layer, and as a negative instance if it does not appear in any layer, corresponding and Kim & Oh (2022). we use a standard uniform random negative sampling strategy, where the ratio is I:I, which means that each positive node pair is matched with one randomly selected non-interacting pair drawn from the set of all unobserved pairs.
all-zero label vector. As in Schlichtkrull et al. (2018)
To generate LLM embeddings for bacteria entities, their genomes are segmented into fixed-length windows (124gth=1024) and fed into DNABERT-2. We average the embedding outputs across windows to obtain a compact genome-leve representation.
In the transductive setting, entities in the training, validation, and test sets overlap, meaning that all nodes are present during training but the edges are partitioned. As result, models can exploit topological context from the training graph when making predictions, and the task is to infer unseen interactions among already observed entities. This setting reflects scenarios where the biological entities are known, but their interaction space is incomplete.
contrast, the zero-shot setting enforces strict disjoint entity-based split, where entities in the validation and test sets entirely excluded from the training graph. These entities do not appear endpoints even as neighbors during training, ensuring that the model cannot rely on structural information about them. Instead, predictions must be made solely from their intrinsic features (e.g., sequence embeddings), without any prior connectivity. The task therefore involves predicting interactions among completely novel entities, simulating realistic discovery scenarios where drugs, genes, proteins enter the system with known
23

---

Published as a conference paper ICLR 2026
interactions. This strict separation guarantees that no information leakage occurs between training and test sets in either setting.
Hyperparameters are selected via grid search, and models are trained using the Adam optimizer. All experiments are conducted on single NVIDIA A100 Tensor Core GPU with 80GB of RAM. The training deatils of CAZI-MBN are presented in Table
For benchmark models using node attributes, compounds are encoded with Morgan fingerprints from SMILES, and genes/genomeex/rottiinn with k-mer features (k = from sequence data. To provide a more detailed comparison, we additionally include results from MLP and XGB benchmarks that use the same sequence LLM embeddings as our model.
For R-GCN and R-GAT, each multiplex layer is treated as distinct relation type. R-GCN performs relation- specific message passing by applying a separate transformation to each relation type and aggregating the resulting messages into unified node embeddings. R-GAT also computes relation-specii attention coefficients on these relation-labeles edges and combines the weighted messages to update node embeddings. In zero-shot setting, where traditional graph-based models cannot inherently predict interactions involving entities with no observed training interactions, we adopt knowledge-distille original graph-based models serve as teacher models, while two-layer MLP classifier chain acts as the student model. This setup facilitates performance comparisons in zero-shot learning.
variants of these models. Specifically, the
Table 9: Training details of CAZI-MBN.
Component
Value/Detail
Optimizer
Adam
Learning rate
le-4
Batch size
256
Number of epoches
10000
Early stopping
Yes
Order of UGT
5
UGT output dimension
128
Readout (CAE)
SAGPool
Latent dimension (knowledge distillation)
32
E.3 SUPPLEMENTAL EXPERIMENTAL RESULTS
E.3.1 EMPIRICAL ANALYSIS
Tables 10
12 and present the performance comparisons between CAZI-MBN and all benchmark models under both transductive ("T"") and zero-shot ("ZS") settings across five MBNs. All metrics are reported in Mean + SD over three repeated trails.
In terms of robustness of CAZI-MBN over multiplex networks with different sparsity levels, our analysis shows that the model consistently maintains strong zero-shot performance even when the structural density of the multiplex graph varies across datasets. There is naturally some impact: if the multiplex graph is denser, the teacher can extract richer structural signals, which can strengthen the distilled representations. Sparsity can limit the amount of structure the teacher learns, but it is not the main bottleneck in our design. We quantify sparsity using the multiplex rate (see Table which ranges from 1.34 percent in DGIdb to 27.10 percent in MetaConserve, representing nearly twenty-fold difference in cross-layer connectivity. Despite this wide variation, CAZI-MBN achieves stable zero-shot AUROC and AUPRC across all five benchmarks. Notably, DGIdb, the dataset with the lowest multiplex rate, still achieves strong zero-shot performance and exceeds
24

---

Published as conference paper ICLR 2026
Table 10: Evaluation of transductive and zero-shot multiplex interaction prediction on DGIdb.
Setting
Model
AUROC
AUPRC
HS
SA
XGB
0.35010.010
0.38940.011
0.579#0.011 0.609+0.009
0.421=0.011
MLP
0.284
0.375++0.00
0.407+10.011 I
XGB (LLM)
0.517=0.012
0.572=#0.009
0.541=0.014
0.488=0.010
MLP (LLM)
0.511+0.011
0.561+0.008
0.536++0.010
0.503+00012
GCN
0.482
0.493#0.000
0.511
0,491
GraphSAGE
0.44710.005
0.467+0.003
0.52740.006
0.473+0.006
Graph Transformer
0.505=00000
0.514+0.005
0.493+0.000
0.508
T
DGI
0.503+0.002
0.507
0.510+0.003
0.500+
MultiplexSAGE
0.532+0.007
0.539:+0.004
0.509
0.507
DMGI
0.547=0.002
0.542++0003
0.533#0.005
0.505=0.002
HDMI
0.501
0.557=10.07
0.540:10.012
0.511
R-GCN
0.536+00.018
0.541
0.530+10.014
0.508
R-GAT
4.0047
0.549++0001
0.521400017
0.514++0.021
CoSMIG
0.517+0.003
0.523
0.522++0.003
0.509+00000
DGCL
0.519+0.005
0.531+0.004
0.527+00007
0.512++0.006
CAZI-MBN
1711500.007
0.0099
0.687+00.011
0.684+0.0115
XGB
0.332100013
0.361
0,51310.013
0.401
MLP
0.258
0.307
0.590+0.011
0.347=0.005
XGB (LLM)
0.487
0.522
0.51710.015
0.496+10.006
MLP (LLM)
0.494
0.005
0.522+0.012
0.492+0.0077
GCN
0.448
4.4094
0.502+00005
0.473
GraphSAGE
0.409
0.40440.004
0.518
0.464-10.005
Graph Transformer
0.49810.011
0.50240.004
0.48610.004
0.50440.007
ZS
DGI
0.48940.004
0.004
0.502+0.003
0.477
MultipleXSAGE
0.51810.000
0.520
0.537+0.009
0.004
DMGI
0.52410.008
0.52110.016
0.52940.004
0.502+10.066
HDMI
0.507
0.536++0.012
0.530+0.011
0.507
R-GCN
0.510=0.015
0.51440.008
0.525+0.028
0.500+0.013
R-GAT
0.512+0.012
0.51840.014
0.519+0.015
0.507
CoSMIG
0.509
0.51440.005
0.51640.013
0.500+0.020
DGCL
0.513
0.515
0.50940.011
0.502+0.004
CAZI-MBN
0.67110.008
0.70910.011
0.68810.009
0.66310.014
*Benchmarks grouped into four categories: (1) Sequence-based (XGB, MLP);(2) Single graph-
based (GCN. GraphSAGE, Graph Transformer, DGI); (3) Multiplex graph- www.d//eelaionnneeeeeeee aware (Multi-
plexSAGE, DMGL, HDMI, R-GCN, R-GAT); and (4) Domain-specific (others).
25

---

Published as conference paper ICLR 2026
Table 11: Evaluation of transductive and zero-shot multiplex interaction prediction on ChEMBL
Setting
Model
AUROC
AUPRC
HS
SA
XGB
0.515+0.013
0.699:000007 0.629#0.012
0.607:100088
0.587=0.012
MLL
0.501
0.657#0.009
0.613=0.010
XGB (LLM)
0.648
0.694
0.735+0.010
0.721+00011
MLP (LLM)
0.62440.008
0.678
0.718+0.011
0.697#0.009
GCN
0.632+0.014
0.700+0.008
0.709+0.004
0.641
GraphSAGE
0.631#0.005
0.69810.012
0.73410.006
0.663+00005
Graph Transformer
0.64410.015
0.715
0.74110.005
0.657+00.12
T
DGI
0.651
0.719
0.738
0.673+0.011
MultiplexSAGE
0.653+0.007
0.709
0.71210.005
0.705
DMGI
0.66110.017
0.74940.008
0.773+0.021
0.712+000114
HDMI
0.663+0.012
0.762400011
0.789+0.012
0.730=0.006
R-GCN
0.647#0.016
0.72540.011
0.760+0.033
0.719#0.023
R-GAT
0.655+0.013
0.742+0.009
0.771+00025
0.722+00010
CoSMIG
0.661
0.732++0.01
0.755+10.009
0.702
DGCL
0.65910.014
0.712
0.739400007
0.698
CAZI-MBN
0.008
0.063
0.88910.014
0.75710.011
XGB
0.481
0.542-10.01
0.60810.014
0.574
MLP
0.48710.012
0.509
0.621
0.581
XGB (LLM)
0.622+0.019
0.712+0.001
0.63740.013
0.616+0.012
MLP (LLM)
0.634
0.728
0..015
0.639+:0011
GCN
0.61210.013
0.702
0.63310.006
0.607#0.011
GraphSAGE
0.624+0.009
0.713+0.018
0.640+0.006
0.611+0.008
Graph Transformer
0.628+0.011
0.720+0.008
0.725+00013
0.628+
ZS
DGI
0.61910.010
0.70940.022
0.708
0.634
MulEipleXSAGE
0.638
0.718
0.71110.008
0.699
DMGI
0.652+0.021
0.045
0.756+0.007
0.711
HDMI
0.648
0.735+100011
0.726+10004
0.716+0.011
R-GCN
0.630+0.013
0.719
0.71410.018
0.687+0.012
R-GAT
0.64310.026
0.731
0.720+0.007
0.708
CoSMIG
0.641+0.011
0.733
0.729
0.683++0.005
DGCL
0.631 00009
0.702+0.018
0.693+0.007
0.675+
CAZI-MBN
0.791+0.011
0.839 0.015
0.857+00011
0.723+0.009
26

---

Published as conference paper ICLR 2026
Table 12: Evaluation transductive and zero-shot multiplex interaction prediction on PINNACLE.
Setting
Model
AUROC
AUPRC
HS
SA
XGB
0.650+0.007
0.644:0.007
0.660:10.023 0.651
MLL
0.676=00008
0.668#0.008
0.656#0.011
0.666=0.016
XGB (LLM)
0.742+00010
0.767++0.00
0.744+0.007
0.734
MLP (LLM)
0.733
0.751
0.69910.010
0.708
GCN
0.019
0.670+0.012
0.666+0.014
0.679+00021
GraphSAGE
0.69210.017
0.677+0.008
0.686:0.000
0.693+0.022
Graph Transformer
0.701
0.704
0.700+0.005
0.726+10.024
T
DGI
0.68811.009
0.695110.010
0.698
0.684
MultiplexSAGE
0.752
0.775
0.763100009
0.74910.011
DMGI
0.002
0.784
0.75910.014
0.062
HDMI
0.773
0.798+0.022
0.776100017
0.70610.011
R-GCN
0.761
0.78640.012
0.75440.024
0.753+0.019
R-GAT
0.759
0.783+0.013
0.760+0.011
0.743
PIPR
0.747
0.759
0.74110.026
0.702
XCAPTS
0.781
0.80440.014
0.726
0.752+10015
CAZI-MBN
0.811
0.845:0.011
0.75110.009
0.772-10.113
XGB
0.62610.023
0.62810.010
0.63610.015
0.639+10.026
MLP
0.65210.015
0.650
0.62610.007
0.642-100022
XGB (LLM)
0.719
0.731
0.688+0.014
0.671
MLP (LLM)
0.014
0.729
0.683+0.000
0.685
GCN
0.63940.017
0.652
0.64910.012
0.65410.011
GraphSAGE
0.673+0.013
0.661
0.668+0.023
0.015
Graph Transformer
0.687+0.019
0.690+0.017
0.680+0.011
0.705+000019
ZS
DGI
0.67410.011
0.678+00022
0.67910.015
0.663+0.020
MulEipleXSAGE
0.753+10019 0.728
0.747+0.022
0.725+000200
DMGI
0.724
0.762+0.024
0.738+0.025
0.738+0.022
HDMI
0.748
0.776+00021
0.757+0.024
0.742+10.014
R-GCN
0.033
0.765
0.74210.012
0.733+100017
R-GAT
0.726
0.75840.020
0.734+0.025
0.735+000116
PIPR
0.721 +0011
0.740
0.716+0.011
0.711+00002
XCAPTS
0.785 + 0.012
0.791+0.015
0.733+0.013
0.739
CAZI-MBN
+.011
0.820 0.008
0.748+0.010
0.763
27

---

Published as conference paper ICLR 2026
Table 13: Evaluation of transductive and zero-shot multiplex interaction prediction on MetaConserve.
Setting
Model
AUROC
AUPRC
HS
SA
XGB MLL
0.61110.006
0.612:40.023
0.617#0.021 0.620#0.017
0.613+0.010
0.617=0.010
0.614#0.015
0.628=0.007
XGB (LLM)
0.662+00.09
0.671+0.018
0.658+00020
0.669+10.013
MLP (LLM)
0.69410.012
0.702:00.015
0.707#0.016
0.703=100110
GCN
0.630+0.007
0.642+0.022
0.628+0.011
0.633+00011
GraphSAGE
0.64910.013
0.646:10.011
0.64410.021
0.64510.012
Graph Transformer
0.663#0.022
0.662:10.017
0.65310.012
0.656:10.007
T
DGI
0.66810.020
0.673
0.66510.010
0.66910.010
MultiplexSAGE
0.691
0.705
0.67810.018
0.672+00011
DMGI
0.717
0.716
0.71910.015
0.720+00013
HDMI
0.725
0.729+0.021
0.71540.006
0.718
R-GCN
0.712
0.71840.010
0.698+0.013
0.703+0.009
R-GAT
0.720+0.028
0.723+0.017
0.714+0.013
0.710+0.014
MolTrans
0.710
0.720+00000
0.719#0.011
0.716+10.020
DrugBAN
0.017
0.71440.012
0.710:000199
0,711
CAZI-MBN
1.0220
0.74410.012
0.779110008
0.65611.001
XGB
0.598
0.591
0.60010.007
0.598110010
MLP
0.60710.016
0.600
0.61210.010
0.62110.015
XGB (LLM)
0.631
0.63740.017
0.635+0.009
0.63340.012
MLP (LLM)
0.659
0.662
0.664
0.672+10.014
GCN
0.61710.015
0.621
0.610+0.012
0.622+10.013
GraphSAGE
0.630+0.010
0.633+0.016
0.627+0.010
0.604
Graph Transformer
0.651+0.007
0.660+0.020
0.652+0.015
0.648+
ZS
DGI
0.646110.018
0.64740.018
0.63810.014
0.640+10.008
MultipleXSAGE
0.697
0.692+0.022
0.685+0.011
0.688+0.012
DMGI
0.702+0.012
0.701
0.698+0.017
0.695+0.000
HDMI
0.709
0.71040.014
0.70410.014
0.707+000009
R-GCN
0.699
0.70240.012
0.68310.016
0.6851000110
R-GAT
0.70340.019
0.705+0.022
0.69110.014
0.699+0.018
MolTrans
0.0887
0.700
0.690+0.007
0.693+-0.012
DruNBAN
0.692+0.017
0.7050
0.707+00015
0.708
CAZI-MBN
4.738+0.011
0.722+00.010
0.749+0.020
0.659
28

---

Published as conference paper ICLR 2026
Table 14: Evaluation of transductive and zero-shot multiplex interaction prediction on TRRUST.
Setting
Model
AUROC
AUPRC
HS
SA
XGB
0.786=10006
0.79440.005 0.799#0.019
0.791
0.788+10007
MLP
0.796=00.008
0.801#0.011
0.803=0.012
XGB (LLM)
0.854+0.019
0.863++0.027
0.852+00001
0.867+
MLP (LLM)
0.801
0.868#0.031
0.85610.017
0.859=0.0021
GCN
0.810+0.008
0.815+0.022
0.812+00008
0.817+
GraphSAGE
0.822+0.010
0.826:10.007
0.81911.008
0.82410.010
Graph Transformer
0.832:100003
0.835+
0.837+0.004
0.830+10.006
T
DGI
0.84110.014
0.84410.011
0.84510.009
0.847100006
MultiplexSAGE
0.860
0.86410.010
0,8581100009
0.866110.005
DMGI
0.871
0.87440.007
0.86910.008
0.872+10.008
HDMI
0,878#0.008
0.879#0.014
0.883+0.005
0.801
R-GCN
0.865+0.011
0.867#0.013
0.861
0.870=00011
R-GAT
0.069
0.871+0.014
0.866+0.018
0.875+
DL-GGI GENER
0.857
0.862+40.02
0.85410.007
0.860#0.005
0.86710.012
0.068
0.865+000100
0.86910.006
CAZI-MBN
0.013
0.872
0.79110.023
0.78411.015
XGB
0.779
0.781=10.006
0.78310.015
0.777+10.014
MLP
0.789
0.790
0.793+000110
0.79441.0006
XGB (LLM)
0.828
0.83640.198
0.842+t0.11
0.831
MLP (LLM)
0.845
0.852
0.837+000021
0.001
GCN
0.801
0.804
0.800#0.006
0.805=000004
GraphSAGE
0.812+0.006
0.817+0.012
0.809+0.009
0.815+0.007
Graph Transformer
0.823+
0.826
0.828+000006
0.820++0.005
ZS
DGI
0.832+0.011
0.83540.014
0.83610.000
0.838+0.0006
MulEipleXSAGE
0.850
0.854+0.010
0.848+0..00
0.855+00000
DMGI
0.861+0.008
0.86440.012
0.858+0.005
0.860+10.008
HDMI
0.868+0.006
0.0690
0.872+0.006
0.87010.004
R-GCN
0.85410.013
0.856+00.09
0,85010.011
0.801
R-GAT
0.859
0.86340.012
0.859+0.019
0.863
DL-GGI
4.006
0.852+40006
0.844
0.851+000006
GENER
0.857 0.007
0.858+0.000
0.855+0.000
0.859+0.007
CAZI-MBN
1899++0.01
0.869 0.013
0.775+0.019
0.764++0.00
29

---

Published as conference paper ICLR 2026
all baseline models, indicating that the method does not rely on dense cross-layer structure. MetaConserve, which also has both low multiplex rate and a small node edge ratio (329 nodes and 377 edges), likewise shows robust performance. These results suggest that CAZI-MBN requires only reasonable amount of relational signal to benefit from multiplex structure, and that the model continues to perform reliably even when multiplex graph is relatively sparse. practice, this implies that CAZI-MBN can operate effectively on real- world biological networks, which are commonly sparse, while still leveraging the available multiplex information to provide more informed predictions.
E.3.2 INTERACTION TYPE-SPECIFIC ANALYSIS.
To assess performance at more detailed level, we focus on minority interaction types across the five MBNs. Accuracy is used as the evaluation metric, and CAZI-MBN is compared against the top-performing benchmark each category (sequence-basee based, single-graph- graph-based, multiplex seris--rrpaaaaaae and domain-speent -ppaii-specific), based their accuracy for each minority interaction type.
prediction accuracy across five datasets in the zero-shot setting. CAZI-MBN consistently outperforms the best benchmark models, demonstrating strong capability in capturing subtle class distinctions and reinforcing its suitability for zero-shot prediction in complex biological networks. This is particularly important in practical applications where rare underrepresented interaction types may be biologically significant.
Figure presents the interaction type-specific
Sequence Based Benchmark
0.501
0.537
0.672
0.571
0.703
Rest Single Graph-Rased Renchmark
548
70101
6.99
0. 0.782
Best Multiplex Graph-Based Benchmark
0.511
U.661
0.80
U.692
0.832
Best Domain-Specific Benchmark
0.660
0.635
0.823
0.733
0.827
CAZI-MBN
0.772
0.791
0.875
0.782
0.863
DGidb
ChemBL
PINNACLE
MetaConserve
TRRUST
Figure Interaction type-specific performance analysis across five datasets, evaluated by prediction accuracy.
30

---

Published as a conference paper ICLR 2026
E.3.3 ABLATION STUDIES
To rigorously evaluate the individual contributions of each core component in the CAZI-MBN framework, we conducted systematic ablation studies by selectively removing module time while keeping the rest of the architecture intact. This approach allows us to isolate the functional impact of each component on overall model performance and better understand how each contributes to the model's capacity to learn from multiplex biological interaction data.
Figure shows the module wise performance drops across all datasets under both transductive and zero- shot settings, with error bars illustrating the variance across interaction types. summary of the ablation configurations provided in Table 15 The detailed results reported in Tables T6 (oaan]y
Ablation studies further validate the necessity of CAZI-MBN's modular architecture. Across all datasets, CAZI-MBN consistently outperforms every ablated variant, and the most significant performance drops occur when the pre-trained LLMs or the CAE module are removed. The LLMs capture fine-grained biochemical semantics across drugs, genes, and proteins, while the CAE module introduces multiplex-awan context- sensitive attention and contrastive alignment that enhance cross-layer consistency. Together, these modules form the backbone of CAZI-MBN's representational power, enabling it jointly model both sequence information and heterogeneous interaction patterns.
CAZI-MBN was specifically designed to integrate high-quality biochemical sequence embeddings with multiplex graph structure and to distill this joint information for generalization. This design choice is particularly critical in the zero-shot setting, where models must predict interactions for entities with no prior neighborhood context. The strong contribution of LLM-based embeddings to overall performance is therefore not surprising, as they provide rich sequence- semantics that directly support generalization. Indeed, when compared against baselines that lack domain speciff LLMs, CAZI-MBN shows substantial improvements in zero-shot prediction, confirming the importance of leveraging modality-specific pre-trained models.
Taken together, these findings demonstrate that CAZI-MBN's strength lies its capacity to integrate comple- mentary modalities, sequence-level semantics and relational structure, into coherent representations. This synergy allows the framework to capture both fine-grained molecular details and higher-order dependencies across interaction layers, yielding robust performance in transductive tasks and strong generalization in zero-shot scenarios.
Table 15: Summary of ablation variants.
Variant
Description
w/o UGT
Remove UGT; use only LLM-derived features as input
w/o LLMs
Replace LLM features with 6-mer and Morgan fingerprints Replace CAE with single GCN per interaction type
w/o CAE
w/o MoE
Replace MoE with a plain MLP per interaction type
w/o Lais
Discard discriminator
w/o Lreg
Discard consensus regularizer and replace with attention-only fusion for Z
31

---

Published as conference paper ICLR 2026
AUROC
AUPRO
HS
SA
Transductive
Zero-Shot
0.15
DGidb
0.10
0.00
wio UST
wib LLMB
wio CAE
wio MoE
wio UGT
wio LLMB
wi CAE
wio MaE
0.20
0.15
ChEMBL
0.10
0.05
0.00
Wio LIGT
wi LLMs
wio GAE
MoE
wio UGT
wi LLMS
wio CAE
wio MaE
0.15
Performane
PINNACLE
0.10
Wio UOT
WIO LLMB
we CAE
wio MoE
wi UGT
wio LLMB
wi CAE
wio MoE
0.20
0.15
MetaConserve
0.05
0.00
wio UCT
WiO LLMB
GAE
wio MoE
UGT
wD LLMe
CAE
wio MaE
0.20
TRRUST
0.10
0.05
wio UCT
wio LLMs
wi CAE
32
weo MoE
Wi UGT
wio LLMS
wi CAE
wio MaE
Figure 6: Module-wise ablation analysis across five MBNs under transductive and zero-shot settings. Bars show the performance drop for four evaluation metrics when removing each module (UGT, LLMs, CAE, MoE), Error bars reflect the variance across interaction types.

---

Published as conference paper ICLR 2026
Table 16: Ablation results in transductive setting across five MBNs.
Dataset
Model
AUROC
AUPRC
HS
SA
w/o UGT
0.666:10.015
0.705=10.008
0.639:10.012
0.634:0.017
w/o LLMs
0.563+0.021
0.579=0.023
0.582:0.016 0.591
w/o CAE
0.601=0.014
0.635=0.018
0.613:00016
0.602#0.012
DGIdb
w/o MoE
0.672:1.019
0.707=0.011
0.637=0.018 0.645#000200
w/o Ldis
0.692+0.022
0.701=0.017
0.673+0.019
0.655#0.022
w/o Treg
0.679+-0.012
0.696+00.01
0.660+00.010
0.644+0.0014
CAZI-MBN
0.715
0.729+0.000
0.687+000111
0.684+0.015
w/o UGT
0.701100010 0,81510.006
0.84410.010
0.740=0.013
w/o LLMs
0.655 0.691=0.020
0.710++00113
0.612+0.019
w/o CAE
0.702=10.008 0.727=0.012
0.778+10001
0.691
ChEMBL
w/o MoE
0.735+1.0114 0.781=00017
0.797
0.701
w/o Lait
0.790-10.023
0.824
0.859+00.011
0.728
w/o Lreg
0.73140.014
0.811=000155
0.826+00012
0.71540.016
CAZI-MBN
0.002
0.863
0.8894100014
0.057+0.011
w/o UGT
0.801+0.009
0.831+00013
0.705
0.749+0.00
w/o LLMs
0.683+0.011 0.699
0.629++0.019
0.609
w/o CAE
0.719=1.014
0.738=00015
0.685+0.008
0.65410.014
PINNACLE
w/o MoE
0.748110.015
0.765-10016
0.707
0.69310.013
w/o Ldis
0.811=0.016
0.832+0.25
0.740+00011
0.742#0.018
w/o Lreg
0.795+0.011
0.818=0.013
0.737+0.013
0.722+0.013
CAZI-MBN
0..11
0.845
0.751
0.772
w/o UGT
0.701 0.705
0.699+0..01
0.661 +0.011
w/o LLMs
0.610=0.018 0,620=0.022
0.601#0.021 0.641
w/o CAE
0.678=0.019 0.679=0.018
0.693+0.010
0.635#0.014
MetaConserve
w/o MoE
0.702+00.016
0.719=0.020
0.729+00008
0.642+0.010
w/o Ldis
0.731=0.011
0.720=0.009
0.737+0.014
0.625#00018
w/o Lreg
0.72710.015
0.71910.014
0.743+00.011
0.637+0.014
CAZI-MBN
0.752+0.020
0.744-0.012
0.779-10.008
0.656=0.014
w/o UGT
0.813+0.013
0.829=00011
0.722+0.010
0.714+0.015
w/o LLMs
0.001
0.738=0.015
0.655+0.012
0.642+0.018
w/o CAE
0..017
0.775+0.020
0.698+0.011 0.701
TRRUST
w/o MoE
0.792
0.011
0.727+0.019
0.720+0.012
w/o Ldie
0.857+0.009
0.81210.014
0.733+0.013
0.730+0.020
w/o Lreg
0.831
0.84010.012
0.72810.011
0.722+00013
CAZI-MBN
0.899
0.019
0.775:10.019
0.764+0.007
33

---

Published as conference paper ICLR 2026
Table 17: Ablation results in zero-shot setting across five MBNs for CAZI-MBN(KD),
Dataset
Model
AUROC
AUPRC
HS
SA
w/o UGT
0.633:10.015
0.66410.010
0.637+0.012
0.625
w/o LLMs
0.527#0.017
0.553=0.025
0.599:00.018
0.573#0.015
w/o CAE
0.589=10.019 0.607=0.020
0.618
0.604=0.012
DGIdb
w/o MoE
0.641=0.012
0.662=0.015
0.639#0.011
0.631#0.011
w/o Ldis
0.643+0.017
0.677+0.009
0.819+0.022
0.690+00011
w/o Treg
0.593+0.012
0.61640.013
0.621+0.011
0.601#0.012
CAZI-MBN
0.671+00008
0.709
0.688+000009
0.663+0.014
w/o UGT
0.744
0.787=0.014
0,0100
0.699:0.017
w/o LLMs
0.644+0.021 0.673=0.027
0.689
0.591#0.015
w/o CAE
0.686=1.011
0.709=0.016
0.756=t0.008
0.667#0.011
ChEMBL
w/o MoE
0.718=100110
0.765+00012
0.791
0.707
w/o Lait
0.772-10.023
0.810=0.019
0.815+0.017
0.693+0.025
w/o Lreg
0.71840.013
0.746#0.014
0.775#0.010
0.686#0.014
CAZI-MBN
0.7018
0.001
0.857+0.011
0.723+0.009
w/o UGT
0.755+0.012
0.018
0.713+0.014
0.725 +.0012
w/o LLMs
0.671 0.685+0.021
0.628++0.012
0.614+0.014
w/o CAB
0.708 0.715=00014
0.691
0.66710.016
PINNACLE
w/o MoE
0.733 0.745=0.011
0.717
0.701
w/o Ldis
0.775= 0.801=00008
0.715+0.022
0.730=0.019
w/o Lreg
0.758+0.011
0.794#0.014
0.732+0.012
0.712+0.013
CAZI-MBN
0.012
0.000
0.748
0.763
w/o UGT
0.692+0.011 0.019
0.719+0.012
0.631 +0.014
w/o LLMs
0.609-0.017 0.634=0.015
0.651
0.591
w/o CAE
0.656-0.014 0.662=0.016
0.687+0.009
0.615#0.011
MetaConserve
w/o MoE
0.703+00.05
0.717=0.018
0.708+0.0111
0.642+0.009
w/o Ldis
0.719=00022
0.693
0.716+000114
0.633#00016
w/o Lreg
0.713+0.014
0.700#0.015
0.735+0.011
0.62940.013
CAZI-MBN
0.738
0.722-0.010
0.749-1.0220
0.653+0.009
w/o UGT
0.848+0.013
0.826=0.016
0.741
0.726+0.012
w/o LLMs
0.742
0.749-0.021
0.662+0.010
0.655+0.014
w/o CAE
0.788+0.019
0.802+40.013
0.709+000014
0.701
TRRUST
w/o MoE
0.824
0.014
0.729+00013
0.740
w/o Ldie
0.86710.019
0.831-0.029
0.732+20.200
0.73540.011
w/o Lreg
0.851+0.012
0.818+0.011
0.729+0.013
0.716+0.014
CAZI-MBN
0.899
0.019
0.775:000019
0.764+0.007
34

---

Published as a conference paper ICLR 2026
E.3.4 CASE STUDY: INTERACTIONS INVOLVING IBD-RELATED GENES AND PROTEINS
To evaluate the effectiveness of our approach real-world biological scenarios, we conducted a case study focused zero-shot multiplex interaction prediction involving IBD-related genes and their protein products. The selected genes, GAPDH, PC, RPS14, OAT, and NDUFABI, were chosen based on their established functional relevance to IBD and their evolutionary conservation from E. coli orthologs, including gapA, accC, rpsK, orn, and These genes represent a diverse set of biological functions, such as energy metabolism, redox balance, immune regulation, and mitochondrial activity, all of which are closely associated with IBD pathogenesis. This focused selection enables a robust evaluation of the model's ability to predict novel, biologically meaningful interactions involving entirely unseen entities.
The genes and their IBD-relevant roles are summarized as follows:
GAPDH (ortholog: gapA): Known for its central role in glycolysis, GAPDH also regulates T-cell function and cytokine production, highlighting its dual role in metabolism and immune modula- tion (Sheng & Wang 2009 Bas et al. 2004 Barber et al. (005).
PC (ortholog: accC): Pyruvate carboxylase plays a key role in gluconeogenesis and epithelial energy metabolism, both of which are affected in IBD (Algieri al. 2016 Ferrer-Picón al., 2020 Tang
ct al. 2015
Bos & Laukens 2020)..
RPS14 (ortholog: rpsK): A ribosomal protein with emerging evidence linking it to immune and
stress response regulation in intestinal inflammation (Sallman & List 2019 Zou & Zhang 2021
Lin el. 2020 Das et al., 2024).
OAT (ortholog: orn): Ornithine aminotransferase regulates amino acid metabolism and redox
balance, with links to epithelial injury in IBD (i et al. 2023: Smith et al. 2021 Lan et al. 2023
Gobert et al., 2004).
NDUFABI (ortholog: fabD); This mitochondrial protein supports fatty acid synthesis and electron
transport and associated with epithelial barrier integrity (Guerbette et al. 2022 Rath et al. 2018
Kim et al. 2021).
For each sataset-specific case study, separate model was trained using a standard zero-shot setting. All biological entities and interactions involving the five specified genes and their protein products were held out the test set. The remaining interactions and associated entities
randomly split into training and validation sets using an 5:1 ratio. This setup ensures that the model does not encounter the held-out genes or their associated links during training, enabling clear evaluation of its zero-shot generalization ability.
Table 18 presents the zero-shot prediction performance of CAZI-MBN across four benchmark datasets, reporting an overall, as well as per-gene and gene product-level interaction statisties. These results evaluate the model's 's ability to generalize to previously unseen entities, specifically those involving five held-out human genes and their bacterial orthologs. None of these genes or their products were included in the training set, allowing for a rigorous assessment of the model's zero-shot capabilities in the context of sparse and novel biological interactions.
CAZI-MBN recovered substantial proportion of known interactions across all evaluated datasets, achieving 82.7% accuracy in DGIdb, 85.7% in TRRUST, 75. % in PINNACLE, and 62.5% in MetaConserve. These datasets collectively span range of biological interaction types, including drug-gene interactions (DGIdb), transcriptional regulatory links (TRRUST), sepesiie- protein-protein interactions (PINNACLE), and evolutionarily conserved co-occurrence patterns (MetaConnserve). The model's consistently strong performance across these diverse settings underscores its robustness and flexibility in handling heterogeneous biological data.
Closer inspection of individual genes reveals that CAZI-MBN maintains strong predictive performance even for held-out genes with relatively few known interactions in the test sets. For example, PC (accC) and
35

---

Published as a conference paper ICLR 2026
Table 18: Per-Gene/Protein Product Statistics Across Datasets
DGIdb
TRRUST
PINNACLE
MetaConserve
34 / 41 (82.9%)
2/ 3 (66.7%)
103 140 (73.6%)
PC(accC)
2/2 (100%)
2 (100%)
RPS14(rpsK)
3/5 (60%)
340 /54 (74.9%)
(AT(om)
1/1 (100%)
2 2 (100%)
11 16 (68.7%)
NDUFABI(faDD)
3 3 (100%)
46 / 57 (80.7%)
5 / 8 (62.5%)
Total
43 52 (82.7%) 6 (85.7%)
500 666 (75.1%)
5 / 8 (62.5%)
Each cell shows: Predicted Actual (Accuracy), where Predicted indicates the number of interactions correctly predicted by the model, and Actual indicates the total number of known interactions involving the given gene or its product. indicates no active interaction with the gene or its product was found in that dataset.
Total interaction counts may equal the of individual gene gene product counts, interactions involve multiple listed genes or their products.
OAT (orn) each exhibited low interaction frequency within DGIdb and TRRUST, yet the model successfully recovered all known interactions for both cases in these datasets. This is noteworthy given that these genes were entirely excluded from training. highlighting the model's ability to generalize to novel genes and their interaction profiles. Although these results reflect only test-time performance, they demonstrate that CAZI-MBN can effectively capture biological relationships across different interaction types, including drug-gene interactions, transcriptional regulation, cell-type specific protein-protein interactions, without relying on prior exposure to the specific biological entities involved. Such flexibility is critical for modeling MBNs, where each biological entity may participate diverse biological processes and be subject to distinct regulatory mechanisms. Overall, these findings highlight the strength of CAZI-MBN in generalizing across both interaction types and gene contexts. Its capacity to make accurate predictions in a zero-shot setting suggests its potential valuable tool for expanding known interaction networks, prioritizing novel gene candidates, and supporting downstream biological discovery.
Several high-confidence predictions made by CAZI-MBN are further supported by existing biomedical literature, reinforcing the biological plausibility of the model's outputs. For example, the model predicts an interaction between GAPDH and Omigapil (DGIdb, pred: 0.912), which is validated by evidence showing that Omigapil inhibits the GAPDH-Sine apoptotic signaling pathway in neuromuscular disorders (Foley ct al., 2024 Zhou al. 2015). Additionally, GAPDH has been shown to chaperone ribosomal protein LI3a. stabilizing the GAIT complex and modulating inflammation-relate predicted interactions between GAPDH and ribosomal proteins in PINNACLE (Jia et al., 2012). The model also identifies numerous interactions involving RPS/4 with other ribosomal proteins (e.g., RPL19, RPS17), which are consistent with its role in ribosome assembly and immune-related translational control (Zhou et al. 2015 Chan et al., 2018 Wang et al. 2022). These literature-backed findings confirm that CAZI-MBN is not only capable of recovering known links but also makes biologically coherent predictions in a fully zero-shot setting.
translation, which aligns with
This case study highlights the effectiveness of multiplex-ave aware zero-shot lcarning frameworks in predicting biologically meaningful interactions for novel or under-characterriedd entities. It reinforces CAZI-MBN's po- tential as a tool for accelerating hypothesis generation and advancing discovery across complex, multilayered biological systems.
36

---

Published as a conference paper ICLR 2026
E.3.5 PARAMETER COUNTS AND GPU TIME
Table 19 reports the dataset sizes, parameter counts, average GPU time, and average runtime per epoch. Because early stopping is used, the total number of training epochs varies across runs. Overall, the student model is substantially smaller and trains much faster than the teacher model. In practice, the student requires only small share of the total GPU time because it has far fewer parameters and does not perform the heavier graph operations. A notable trend is that teacher runtime grows more with the number of interaction types than with the number of nodes, since separate graph transformer is instantiated for each interaction type.
Table 19: Runtime and parameter statistics across datasets for teacher and student models.
MetaConserve
DGIdb
TRRUST
PINNACLE
ChEMBL
# Nodes
329
1,846
2,862
7,044
9,368
# Interaction Types
4
5
12
3
Teacher Total GPU Hours
0.463 h
3.191 h
2.137 h
31.34 h
8.456 h
Teacher Time/Epoch
0.574 s
4.332 s
3.903
115.037
43.201
Student Total GPU Hours
0.00109
0.0567 h
0.0098 h
0.4275 h
0.114 h
Student Time/Epoch
0.005 s
0.129 S
0.078
3.802
1.311 S
Teacher Parameters
2016.820 M
3430.584 M
2062.506 M
6045.610 M
2068.336 M
Student Parameters
1.189 M
1.734 M
1.041
3.568 M
1.041 M
E.3.6 TTRUST: POST-HOC PER-LABEL CALIBRATION
To assess whether more flexible thresholding strategy could improve the multi-label metrics for datasets which lags on HS and SA, we conducted a post-hoc per-label calibration experiment on TRRUST. For each interaction type, we swept thresholds from 0.2 to 0.8 in increments of 0. and selected the value that maximized validation accuracy for each interaction type. This allows each label to adopt a threshold better aligned with its own score distribution. The same post-hoc per-label calibration strategy is also applied the baselines to ensure fair comparison.
Table 20 shows the evaluation results of transductive and zero-shot multiplex interaction prediction on TRRUST after post-hoc per-label calibration. After applying per-label calibration, we find that most models achieve higher HS and SA, with CAZI-MBN showing significant improvement TRRUST well. This indicates that more flexible thresholding can better accommodate differences in label prevalence and score distributions across interaction types. Allowing each label use its own decision boundary provides better match to this heterogeneity, suggesting that more adaptive thresholding strategies may further enhance multi-label evaluation multiplex interaction prediction.
E.3.7
ATTENTION WEIGHTS
In CAE, layer-level context-awar consensus attention aggregates the layer-specifie node features into shared representation H and its negative counterpart H. A contrastive consensus regularizer then learns multiplex-widd consensus embedding Z by maximizing agreement with zl and minimizing disagreement with H, enabling the model to form a layer-informed representation.
Figure shows the average layer-level attention weight distributions for all five datasets. TRRUST, ChEMBL, DGIdb, and MetaConserve each have one or two interaction types that receive most of the weight, which indicates that the model relies small set of strong signals in these datasets. PINNACLE contains more interaction types, which naturally reduces the variability of each individual weight, although some differences in emphasis still visible. These patterns support the purpose of using attention weights: different layers
37

---

Published as conference paper ICLR 2026
Table 20: Evaluation of transductive and zero-shot multiplex interaction prediction TRRUST (Post-Hoc
Label Calibration).
Setting
Model
AUROC
AUPRC
HS
SA
XGB
0.786-10.006
0.788110.017 0.799-0.011
0.791
0.792-40.014
ML.P
0.796++0.008
0.809+0.013
0.812+0.018
GCN
0.810=0.008
0.815:0.022
0.821
0.826+0.010
GraphSAGE
0.011
0.826=0.007
0.824+00014
0.829+00015
Graph Transformer
0.832++0.005
0.835=0.015
0.841
0.834+0.012
DGI
0.84140.0114
0.84410.011
0.851
0.853+0.011
T
MultipleSAAGE
0.860=0.0188
0.86410.010
0.872+0.011 0.861
DMGI
0.801
0.87410.007
0.875
0.006
HDMI
0,878:10.008
0.879#10.014
0.88710.009
0.884
R-GCN
0.865110.011
0.867+00013
0.872+:0.018
0.881
R-GAT
0.868
0.871=0.014
0.878=0.020
0.885
DL-GGI
0.857+00006
0.862+0.012
0.803
0.8614
GENER
0.867100012
0.86810.006
0.87310.011
0.876
CAZI-MBN (Per-Label Calibration)
0.005
0.872 0.008
0.015
0.891+
CAZI-MBN (Unified Threshold)
0.905+00.013
0.872+ 0.008
0.791
0.784:
XGB
0.779:t0.012
0.781
0.783+-0.018
0.789+0.014
MLP
0.789-10.010
0.790=0.014
0.799100020
0.801
GCN
0.001
0.80410.010
0.81210.014
0.815+0.013
GraphSAGE
0.002
0.817+0.012
0..181
0.821+00016
Graph Transformer
0.823+00008
0.826+0.016
0.837+0.020
0.833+0.018
DGI
0.832+00001
0.835+0.014
0.848
0.849+0.015
zs
MultipleXSAGE
0.850+00.007
0.854+0.010
0.006
0.85
DMGI
0.801
0.86410.012
0.860
0.869+0.017
HDMI
0.800
0.86910.010
0.88410.019
0.882+0.020
R-GCN
0.85410.013
0,856+0.009
0.86210.022
0.870+0.021
R-GAT
0.859::0.015
0.863#0.012
0.86910.018
0.872+0.017
DL-GGI
0.848
0.852+0.006
0.86010.020
0.863-10.016
GENER
0.857=00007
0.858
0.858
0.862+00008
CAZI-MBN (Per-Label Calibration)
0.899-10.017
0.869 0.013
0.882
0.013
0.884
CAZI-MBN (Unified Threshold)
0.899
1.899-0.015
0.775 0.019
0.764
38

---

Published as conference paper ICLR 2026
TRRUST
ChEMBL
DGidb
Reprosston
Adtivation
20 5%
25 0h9
Medulsior
28, 8%S
Othor
Apon
01.4%
Unkrown
Acthvato
PINNACLE
MetaConserve
Gullell (50)
Compytohacteriies
Panath
13.2%
Goblet (L.)
11.1%
Ertersoyle
54
Hil pan
13 2%
0.2%
Entorecyte
CD4 Momory
4.4%
9.6%
28.7%
23.3%
B 0%
Memory
Rusebacteriales
Vii lonel sot
Cel
Figure 7: Attention weight distributions across all five multiplex biological datasets. Each pie chart shows the relative importance assigned to different interaction types within a dataset.
39

---

Published as a conference paper ICLR 2026
contribute uneven amounts of signal due variation in noise levels, edge density, and biological relevance. A fixed or uniform weighting scheme implicitly assumes that all layers are equally informative, which is often violated in multiplex biological networks. Learning attention weights allows the model to adaptively modulate each layer's s influence by down-weighting noisy weakly aligned layers and emphasizing those with stronger structural consistency. This produces consensus embedding that better captures the dominant cross-layer relational patterns.
40


Reviews:
[{"review_id": "1rDXHgt3LP", "reviewer": "Reviewer_7pwj", "summary": "This paper proposes CAZI-MBN, a topology-aware teacher–student framework for interaction prediction on multiplex biological networks (MBNs), targeting in particular the zero-shot case where new entities have no observed neighborhood (e.g., new drugs, uncharacterized genes). The teacher model combines (i) domain LLM–based sequence embeddings (ChemBERTa-2, DNABERT-2, ESM-2), (ii) topology embeddings from a Unified Graph Tokenizer over the supra-adjacency, and (iii) a Context-Aware Enhancement (CAE) module with graph transformers and contrastive/consensus objectives. A Mixture-of-Experts head is used for multi-label interaction prediction across relation types. A topology-agnostic student is then distilled to match the teacher’s latent representations and task outputs, so that it can make zero-shot predictions from sequence only. The authors also curate five multiplex benchmarks (DGIdb, ChEMBL, PINNACLE, MetaConserve, TRRUST) to standardize evaluation in this setting.", "questions": "1. Please report GPU hours, parameter counts (teacher vs. student), UGT rank, and runtime per epoch on the largest dataset.\n\n2. Can you add or report a native multiplex/heterogeneous GNN baseline to show that CAZI-MBN is competitive even against relation-aware models?\n\n3. For datasets where CAZI-MBN lags on HS/SA, how were decision thresholds chosen (global vs. per-label vs. per-dataset)? Would per-label calibration close the gap?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The teacher-student distillation idea is well matched to the zero-shot MBN problem: topology is used only when available, but its signal is transferred to a sequence-only student so that unseen entities can still be scored. \n\n2. The architecture is thoughtfully composed (LLM sequences + UGT topology + CAE alignment + MoE for multi-label), and the ablations suggest that CAE and LLM features contribute the most.\n\n3. The IBD case study shows that the method can surface biologically plausible candidates, not just improve AUROC.", "weaknesses": "1. The full teacher pipeline is heavy: it requires multiple domain LLMs, SVD/factorization on a supra-adjacency matrix for UGT, and graph transformers within CAE. The paper does not report runtime / GPU hours / UGT rank on the largest MBN, so it is hard to assess scalability to very large networks.\n\n2. On smaller / skewed datasets such as TRRUST, the model improves AUROC but underperforms some baselines on Hamming Score / Subset Accuracy, suggesting that label imbalance, MoE calibration, or the thresholding strategy may affect multi-label metrics. A short analysis would make the empirical section tighter.\n\n3. Several baselines are adapted to the multiplex setting by training a separate classifier per interaction type. This is a reasonable but relatively weak adaptation. Adding at least one heterogeneous GNN (e.g., an R-GCN-style model or a multi-head GAT over relation types) would strengthen the comparison.", "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": "RTPchaRFjZ", "reviewer": "Reviewer_Xr4R", "summary": "This paper tackles zero-shot interaction prediction in multiplex biological networks by combining domain-specific sequence embeddings with a topology-aware graph tokenizer, a context-aware enhancement module, and teacher–student distillation. Across five curated multiplex benchmarks and both transductive and zero-shot settings, the method reports consistent gains in AUROC/AUPRC over single-graph, multiplex, and domain-specific baselines, with ablations attributing the largest lift to LLM embeddings.", "questions": "- Please specify the exact model versions/checkpoints for the sequence LLMs (e.g., ESM-2 model size).\n\n- How is edge/relational heterogeneity handled in the graph transformer—via relation-specific parameters, type encodings, or layer separation?\n\n- How are edge perturbations performed?\n\n- Detail the MoE design (experts, gating, placement); is it a Transformer-MoE or a lightweight MLP-expert setup?\n\n- Describe the zero-shot split protocol.\n\n- Where are the MolTrans results referenced as a domain baseline?\n\n- How are bacterial entities embedded (which LLM/preprocessing)?\n\n- Please add error bars to Figure 4 to reflect variability across interaction types.\n\n- Clarify how graph baselines are adapted to zero-shot.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The authors evaluate on multiple benchmarks spanning different biological domains, suggesting improvements that generalize beyond a single dataset.\n\n- The training/inference workflow is clearly presented and makes the role of each component (UGT, CAE, MoE, KD) easy to follow.", "weaknesses": "- Figure 3’s dataflow appears inconsistent with the algorithm: pooling is depicted in parallel with the graph transformer, whereas the text implies a post-transformer operation.\n\n- Several attention mechanisms are introduced without a clear design rationale; reporting layer-wise attention weights (e.g., across multiplex layers) would help justify the need for attention aggregation.\n\n- Some modules resemble standard variants (e.g., the “node-level context-aware attention” aligns with common attention aggregation); please delineate what is novel vs. adapted.\n\n- All benchmarks are curated by the authors, yet basic dataset statistics and curation decisions (interaction counts by type, inclusion/exclusion filters, handling of homogeneous edges within MBNs) are missing and should be documented.\n\n- For ChEMBL, specify the exact assay criteria/IDs used to build the antibiotic response benchmark; the current size seems very small relative to the database.\n\n- Several baselines may be underpowered: e.g., MLP/XGB should also use the same sequence LLM embeddings to provide a fairer comparison (especially in zero-shot). Also clarify any hyperparameter search to avoid bias.", "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": "Ws0qIwet2n", "reviewer": "Reviewer_EFYv", "summary": "In this study, the authors proposed a CAZI-MBN (Context-Aware and Zero-shot Interaction prediction in Multiplex Biological Networks) model framework for multiplex biological interaction prediction. The evaluation results showed improved performance.", "questions": "It is unclear how many negative data was selected for the model evaluation?\nFor the zero-shot setting, will the performance heavliy based on the density of the available positive controls?\nIn the datasets, it is unclear how many interactions (in addition to the no. of nodes)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The CAZI-MBN (Context-Aware and Zero-shot Interaction prediction in Multiplex Biological Networks) model framework for multiplex biological interaction prediction is kind of new. \nThe evaluation results showed improved performance.", "weaknesses": "For the interactome data, there are much more negative data (no interactions). It is unclear how was the negative data selected and evaluated using the proposed model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)