ArticlePLoS computational biology2019
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 222 papers.
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222 citing papers in PubMed.
- Genome-wide-scale prediction of compound-protein interactions using foundation and language models based on three-dimensional structures of compounds and proteins.Bioinformatics (Oxford, England) · 2026Article
- Machine learning for discovering anticancer compounds from plants and elucidating their mechanisms.Journal of computer-aided molecular design · 2026Review
- KAN-GIN: Adaptive Nonlinear Molecular Representation Learning for Drug-Target Affinity Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- A Length-Adaptive LLM-Enhanced Method for Drug-Target Interaction Prediction.International journal of molecular sciences · 2026Article
- Contextual Representation Learning With ResNet Refinement and Deep Classification for Enhanced Drug Target Interaction Prediction.Chemical biology & drug design · 2026Article
- Article
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- DCI-SiteDTA: drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block.BMC bioinformatics · 2026Article
- MAISNet: a multi-species integrated graph neural network for acetylcholinesterase inhibitor screening.Bioinformatics (Oxford, England) · 2026Article
- DeepMCL-DTI: predicting drug-target interactions using multi-channel deep learning with attention mechanism.Molecular diversity · 2026Article
- SynerDTI: a synergistic deep learning framework for drug-target interaction prediction via global feature coordinated attention mechanism.Molecular diversity · 2026Article
- MFDR-DTI: Multisource Feature Decoupling and Dual-Level Reorganization Fusion for Accurate Drug-Target Interaction Prediction.ACS omega · 2026Article
- CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov-Arnold network.Journal of cheminformatics · 2026Article
- A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction.Cell reports methods · 2026Article
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- Multi-to-uni modal knowledge transfer pre-training for molecular representation learning.Nature communications · 2026Article
162 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.
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