# DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

arXiv: https://arxiv.org/abs/1811.02114 · HTML: https://mlsanity.com/paper/1811.02114/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 30 of 46)

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- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions](https://mlsanity.com/paper/2101.12547/) (2021)
- [PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction](https://mlsanity.com/paper/2402.08198/) (2024)
- [Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity](https://mlsanity.com/paper/2203.11458/) (2022)
- [Drug-Target Interaction Prediction with Graph Attention networks](https://mlsanity.com/paper/2107.06099/) (2021)
- [Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling](https://mlsanity.com/paper/2209.13014/) (2022)
- [A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2405.14545/) (2024)
- [SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning](https://mlsanity.com/paper/2510.16474/) (2025)
- [Associative Learning Mechanism for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2205.15364/) (2022)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders](https://mlsanity.com/paper/2310.02508/) (2023)
- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
- [FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning](https://mlsanity.com/paper/2410.15010/) (2024)
- [DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search](https://mlsanity.com/paper/2507.07426/) (2025)
- [FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation](https://mlsanity.com/paper/2311.02326/) (2023)
- [HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2404.10561/) (2024)
- [Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models](https://mlsanity.com/paper/2407.00111/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions](https://mlsanity.com/paper/2504.04654/) (2025)
- [Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction](https://mlsanity.com/paper/2507.20925/) (2025)
- [Multimodal Regression for Enzyme Turnover Rates Prediction](https://mlsanity.com/paper/2509.11782/) (2025)
- [PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening](https://mlsanity.com/paper/2307.01066/) (2023)
- [Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction](https://mlsanity.com/paper/2501.16391/) (2025)
- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)
- [Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning](https://mlsanity.com/paper/2601.05792/) (2026)
- [ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening](https://mlsanity.com/paper/2102.06086/) (2021)

## Abstract (arXiv)

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 are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found 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.