ArticleInternational journal of molecular sciences2023
A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.Frontiers in bioinformatics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI.Nature communications · 2025Article
- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.International journal of molecular sciences · 2024Article
- CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.Journal of chemical information and modeling · 2024Article
- DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning.Database : the journal of biological databases and curation · 2024Article
- An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.Journal of cheminformatics · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (
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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.