ArticleBioinformatics (Oxford, England)2023
MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 35 citations in OpenAlex.
- Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.iScience · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.BMC bioinformatics · 2026Article
- Advanced drug-target interaction prediction using convolutional graph attention networks in expert systems.Molecular diversity · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Predicting drug and target interaction with dilated reparameterize convolution.Scientific reports · 2025Article
- Article
- Leveraging multimodal learning for enhanced drug-target interaction prediction.Frontiers in pharmacology · 2025Article
- Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy.Computational and structural biotechnology journal · 2024Article
- Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.BMC biology · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2024Article
- CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.BMC bioinformatics · 2024Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationScreening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework.
resultsWe developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.