ArticleJournal of cheminformatics2022
Sequence-based prediction of protein binding regions and drug-target interactions.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- LINKER:Journal of chemical information and modeling · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- MFDR-DTI: Multisource Feature Decoupling and Dual-Level Reorganization Fusion for Accurate Drug-Target Interaction Prediction.ACS omega · 2026Article
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Enhancing monoclonal antibody diversity by integrating bulk sorting and machine learning.Biochemistry and biophysics reports · 2025Article
- A generative framework for enhancing drug target interaction prediction in drug discovery.Scientific reports · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- AI-guided discovery and optimization of antimicrobial peptides through species-aware language model.Briefings in bioinformatics · 2025Article
- Charting γ-secretase substrates by explainable AI.Nature communications · 2025Article
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- SP-DTI: subpocket-informed transformer for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain.PeerJ. Computer science · 2025Article
- Machine learning approaches for predicting protein-ligand binding sites from sequence data.Frontiers in bioinformatics · 2025Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- Exploring the potential of compound-protein complex structure-free models in virtual screening using BlendNet.Briefings in bioinformatics · 2024Article
- Article
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Authors and funding
2 authors.
Funding
Abstract
Identifying drug-target interactions (DTIs) is important for drug discovery. However, searching all drug-target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model's performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein-ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence-ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub ( https://github.com/GIST-CSBL/HoTS ).
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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.