Skip to content
Log in
Open access

ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug–target interaction prediction

Hongyang Yang Xiucai Ye Huipu Han Tetsuya Sakurai
Jul 2026 · Frontiers in Chemistry · Vol 14 · 0 citations · 50 references
Medicine

TL;DR

ColdstartMHDTI is proposed, a two-stage framework for heterogeneous-graph-based DTI prediction that integrates sequence-derived structural representations with local and global relational information and supports candidate prioritization for downstream screening and evidence-guided hypothesis generation.

Abstract

Motivation Accurate drug–target interaction (DTI) prediction remains difficult for underexplored drugs and targets, especially when available interaction evidence is sparse. Existing approaches often focus either on pairwise molecular representations or on heterogeneous biomedical graph topology, making it difficult to effectively integrate structure-derived representations with multi-relational contextual evidence. Result We propose ColdstartMHDTI, a two-stage framework for heterogeneous-graph-based DTI prediction that integrates sequence-derived structural representations with local and global relational information. Specifically, drug SMILES and target sequences are encoded by pretrained transformer models, while one-hop heterogeneous relations are captured through self-supervised DistMult embeddings. These representations are then fused through a meta-path-guided module that models ordered meta-path instances and aggregates them with cross-attention for interaction scoring. Across two benchmark datasets, ColdstartMHDTI shows consistent improvements under warm-start and entity-disjoint settings, with particularly strong performance for underexplored drugs and targets. It also remains robust under more imbalanced evaluation protocols with 1:5 and 1:10 positive-to-negative ratios. In addition to standard classification performance, ColdstartMHDTI supports candidate prioritization for downstream screening and evidence-guided hypothesis generation. Case studies on ESR1, EGFR, and Parkinson’s disease further demonstrate its practical utility, with the Parkinson’s disease analysis additionally highlighting strong per-drug target ranking performance.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Jul 2026

CrossSG-DTA: Synergizing Sequence Semantics and Graph Structures via Cross-Attention for Drug-Target Affinity Prediction.

A multi-modal deep learning framework to predict drug-target affinity by integrating sequence semantics with graph structural information and design a new symmetric dual cross-attention fusion mechanism for drugs and targets.

Wei Lan, Tian Huang, Guohang He et al. · 0 citations
Open access Jul 2026

GraphTransDTI: A novel hybrid framework combining graph transformer and CNN-BiLSTM for enhanced Drug-Protein Interaction prediction

This work proposes GraphTransDTI, a synergistic hybrid framework that integrates a Graph Transformer to represent drug graph structures, a CNN-BiLSTM network to encode protein sequence context, and a Cross-Attention mechanism to model cross-domain interactions.

Vang V. Le, Mai Thi Anh Nhu, Pham Truong Viet Thong · 0 citations
Jul 2026

A Structure-Aware Multimodal Framework for Drug–Target Interaction Prediction via Heterogeneous Graph Learning

Predicting drug–target interactions is critical for drug discovery, yet many deep learning methods overlook atom–residue–level relationships, so PHGDTI is proposed, a multimodal framework that integrates sequence and structural cues for binding prediction.

Hua Qian, Deng Pan, Liangpeng Nie et al. · 0 citations
Open access Jul 2026

EBD-DTI: Episodic Bridge Diffusion for Zero-Shot Cold-Start Drug-Target Interaction Prediction

Predicting drug–target interactions (DTI) for entirely unseen drugs or proteins—the cold-start problem—remains a critical challenge in computational drug discovery. While sequence-based methods naturally support zero-shot generalization, they often ignore relational topology, and existing graph-based approaches either rely on global diffusion that blurs the boundary between inductive and transductive evaluation or require a few known interaction samples at test time (few-shot). We present EBD-DTI, a framework that enables zero-shot inference in graph-based DTI models without requiring any known interactions for unseen entities. The key innovation is episodic cold-start training : at each epoch, a random subset of training entities is masked and treated as pseudo-cold, forcing the model to learn cold-start inference with explicit gradient supervision. A bridge-conditioned local subgraph, together with multi-hop diffusion, provides cold entities with relational context from their nearest observed neighbors. Experiments on three benchmarks (BioSNAP, BindingDB, and DrugBank) demonstrate that EBD-DTI achieves competitive or superior performance compared to state-of-the-art methods under strict zero-shot evaluation, with episodic training improving AUC by up to 12%.

Jiongxin Liu, Jiameng Le, Chuanru Wei et al. · 0 citations
Aug 2026

A Novel Graph Transformer Framework for Predicting Drug-Disease Associations with Structural Awareness.

SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.

Bowei Zhao, Hui Zhao, Yu-an Huang et al. · 0 citations
Aug 2026

GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.

Experiments show that GraESM-FuseDTA achieves competitive overall performance and consistent advantages in ranking-oriented and variance-explanation metrics across warm start, drug cold start, target cold start, and strict pair cold start settings.

Jun-Wen He · 0 citations