# WideDTA: prediction of drug-target binding affinity

arXiv: https://arxiv.org/abs/1902.04166 · HTML: https://mlsanity.com/paper/1902.04166/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

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

- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions](https://mlsanity.com/paper/2101.12547/) (2021)
- [Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction](https://mlsanity.com/paper/2103.12983/) (2021)
- [Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity](https://mlsanity.com/paper/2201.06872/) (2022)
- [SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction](https://mlsanity.com/paper/2206.07015/) (2022)
- [SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning](https://mlsanity.com/paper/2510.16474/) (2025)
- [GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction](https://mlsanity.com/paper/2307.08989/) (2023)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps](https://mlsanity.com/paper/2310.04017/) (2023)
- [Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models](https://mlsanity.com/paper/2407.00111/) (2024)
- [HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks](https://mlsanity.com/paper/2504.02014/) (2025)
- [An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention](https://mlsanity.com/paper/2109.08360/) (2021)
- [GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU](https://mlsanity.com/paper/2204.11857/) (2022)
- [Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective](https://mlsanity.com/paper/2407.04055/) (2024)
- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)
- [DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation](https://mlsanity.com/paper/2505.11529/) (2025)
- [Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2509.11046/) (2025)
- [HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2604.23115/) (2026)
- [CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction](https://mlsanity.com/paper/2111.09467/) (2021)
- [Insights into performance evaluation of com-pound-protein interaction prediction methods](https://mlsanity.com/paper/2202.00001/) (2022)
- [ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections](https://mlsanity.com/paper/2303.11434/) (2023)
- [MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactions](https://mlsanity.com/paper/2306.08907/) (2023)
- [Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning](https://mlsanity.com/paper/2309.16685/) (2023)
- [Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors](https://mlsanity.com/paper/2407.15202/) (2024)
- [LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction](https://mlsanity.com/paper/2603.14792/) (2026)
- [SPADE: Faster Drug Discovery by Learning from Sparse Data](https://mlsanity.com/paper/2605.05370/) (2026)
- [Chemical and geometric representation fidelity improves drug--target affinity prediction](https://mlsanity.com/paper/2609.13230/) (2026)
- [DPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Prediction](https://mlsanity.com/paper/2609.22184/) (2026)
- [Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models](https://mlsanity.com/paper/2609.34921/) (2026)

## Abstract (arXiv)

Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based information sources, namely the protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words to predict binding affinity. WideDTA outperformed one of the state of the art deep learning methods for drug-target binding affinity prediction, DeepDTA on the KIBA dataset with a statistical significance. This indicates that the word-based sequence representation adapted by WideDTA is a promising alternative to the character-based sequence representation approach in deep learning models for binding affinity prediction, such as the one used in DeepDTA. In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model. Interestingly, however, using only domain and motif information to represent proteins achieved similar performance to using the full protein sequence, suggesting that important binding relevant information is contained within the protein motifs and domains.