KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction
Authors: Jiayu Qin, Zhengquan Luo, Guy Tadmor, Changyou Chen, David Zeevi, Zhiqiang Xu
Organizations: Department of Computer Science and Engineering, University at Buffalo, USA · Mohamed bin Zayed University of Artificial Intelligence, UAE · Weizmann Institute of Science, Israel
Predicting molecule-protein interactions (MPIs) is a fundamental task in computational biology, with crucial applications in drug discovery and molecular function annotation. However, existing MPI models face two major challenges. First, the scarcity of labeled molecule-protein pairs significantly limits model performance, as available datasets capture only a small fraction of biological relevant interactions. Second, most methods rely solely on molecular and protein features, ignoring broader biological context such as genes, metabolic pathways, and functional annotations that could provide essential complementary information. To address these limitations, our framework first aggregates diverse biological datasets, including molecular, protein, genes and pathway-level interactions, and then develop an optimal transport-based approach to generate high-quality pseudo-labels for unlabeled molecule-protein pairs, leveraging the underlying distribution of known interactions to guide label assignment. By treating pseudo-labeling as a mechanism for bridging disparate biological modalities, our approach enables the effective use of heterogeneous data to enhance MPI prediction. We evaluate our framework on multiple MPI datasets including virtual screening tasks and protein retrieval tasks, demonstrating substantial improvements over state-of-the-art methods in prediction accuracies and zero shot ability across unseen interactions. Beyond MPI prediction, our approach provides a new paradigm for leveraging diverse biological data sources to tackle problems traditionally constrained by single- or bi-modal learning, paving the way for future advances in computational biology and drug discovery.
Figures & tables
Figure 1: Overview of KGOT. (A) Knowledge integration (B) Supervised score learning (C) Pseudo-label generation (D) KG augmentation & link prediction
Table 1: Results on the DUD-E virtual screening benchmark (zero-shot setting). Higher is better for all metrics. Our OT + KG framework outperforms both traditional docking methods and modern learning-based approaches across all evaluation metrics.
Table 2: Results on the LIT-PCBA benchmark (zero-shot setting). Our method achieves the best performance across all metrics, illustrating its robustness on this more challenging dataset.
Method
Hits@1
Hits@3
Hits@5
PairRE
10.0%
17.0%
21.4%
PairRE + KGOT
10.9%
20.5%
26.4%
RotatE
48.5%
61.6%
66.6%
RotatE + KGOT
52.0%
63.9%
68.0%
MuRE
15.9%
24.7%
31.0%
MuRE + KGOT
13.7%
25.9%
31.2%
Table 3: Knowledge graph link prediction performance (Hits@K) with and without KGOT generated pseudo-label augmentation.
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Method
Unified multimodal KG
KG denoising
Multi-task learning
Global OT pseudo-labeling
KG-MTL Ma et al. [2022]
×
×
✓
×
BioKDN Ma et al. [2024]
×
✓
×
×
KGOT (ours)
✓
×
×
✓
Appendix
Table 4: Qualitative comparison between representative KG-based molecular interaction prediction methods and KGOT. KGOT targets weakly-/unlabeled MPI retrieval via global OT pseudo-labeling and KG write-back, rather than multi-task supervised learning or KG denoising.
Head Entities
Tail Entities
Type
Quantity
Type
Quantity
UNIPROT
5,956,325
GO
3,191,321
CHEBI
336,374
CHEBI
1,678,407
KEGG
92,184
PFAM
792,235
GO
89,235
KEGG_KO
407,307
EC
8,459
EC
304,428
Appendix
Table 5: Entities Distribution, where KMODUL represents KEGG_MODUL, KPATHWAY represents KEGG_PATHWAY, KCOMPOUND represents KEGG_COMPOUND, and KMODULE represnts KEGG_MODULE.
Table 8: Performance of the scoring model with OT-based loss vs. contrastive InfoNCE loss. OT loss yields a modest but consistent improvement in link prediction metrics.
Strategy
Hits@5 (%)
No pseudo-label augmentation
68.5
Random pseudo-labels (matched count)
66.0
Top- k per protein ( k=5 )
72.0
OT without similarity ( λ=0 )
73.8
OT with high entropy ( ϵ=0.1 )
73.0
OT + similarity ( λ=0.1,ϵ=0.01 )
74.9
Appendix
Table 9: Ablation on pseudo-label generation (illustrative Hits@5 on link prediction).
Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning powerful protein representations but neglect designing specialized classification heads. They mainly rely on generic aggregating methods like concatenation or dot products, which lack biological insight. Motivated by the biological "L3 rule", where multiple length-3 paths between a pair of proteins indicate their interaction likelihood, our study addresses this gap by designing a biologically informed PPI classifier. In this paper, we provide empirical evidence that popular PPI datasets strongly support the L3 rule. We propose an L3-path-regularized graph prompt learning method called L3-PPI, which can generate a prompt graph with virtual L3 paths based on protein representations and controls the number of paths. L3-PPI reformulates the classification of protein embedding pairs into a graph-level classification task over the generated prompt graph. This lightweight module seamlessly integrates with PPI predictors as a plug-and-play component, injecting the interaction prior of complementarity to enhance performance. Extensive experiments show that L3-PPI achieves superior performance enhancements over advanced competitors.
Ziqi Gao, Chenyi Zi, Zijing Liu +3
Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou) · IDEA Research +1
Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that have no observed PPI edges during training, where models relying on network topology alone often lose useful context. This paper presents \method, a multimodal representation framework for cold-start PPI prediction. \method\ combines region-aware protein sequence encoding with four protein-centered biomedical knowledge graphs, including protein-drug, protein-disease, protein-miRNA, and protein-lncRNA associations. The sequence branch extracts contextual representations from structurally informed sequence regions, while graph attention encoders learn modality-specific protein embeddings from sparse biomedical associations. A bridge reconstruction objective regularizes graph learning by recovering shared protein-entity associations, and a pair-level gating module adaptively integrates sequence and graph evidence for each candidate protein pair. Experiments on two benchmark datasets under novel-old and novel-novel cold-start settings show that \method\ consistently outperforms competitive sequence, network, and knowledge-graph baselines across ACC, F1, AUC, AUPR, and MCC.
Wenbo Zhang
College of Computer and Information Science, Southwest University, Chongqing, Chongqing, China
Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the hierarchical organization of proteins, particularly meso-scale motifs that critically regulate PPIs, and fail to effectively integrate sequence, structure, and function modalities. To address these limitations, we propose MMM-PPI, a Hierarchical Motif-based Multi-Modal protein Encoder for PPI Prediction that constructs PPI embeddings in a bottom-up multi-modal manner across three scales. At the micro-scale, we encode three modal residue features; at the meso-scale, a novel multimodal motif encoder aggregates residues into spatially-informed motif embeddings; at the macro-scale, a multimodal protein encoder integrates motifs into protein embeddings by jointly modeling motif importance and inter-modal correlations. The pre-trained encoder can be used off-the-shelf for large-scale PPI prediction. Extensive experiments on multiple PPI datasets show that MMM-PPI outperforms state-of-the-art multi-label PPI prediction models, particularly under challenging data partitions and limited data scenarios. Codes are in https://github.com/yzf-code/MMM-PPI.
Zaifei Yang, Samuel Ping-Man Choi, James Kwok
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. · Lee Shau Kee School of Business and Administration, The Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong.