mlsanityData to 7 Oct 2026

WideDTA: prediction of drug-target binding affinity

arXiv 1902.04166 ↗First posted 4 Feb 2019q-bio.QM cs.LG stat.MLcited by 30

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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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 6; 2023: 5; 2024: 4; 2025: 5; 2026: 6.

30 papers.

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  1. 2022Interpretable bilinear attention network with domain adaptation improves drug-target predictioncited by 39
  2. 2021BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactionscited by 12
  3. 2021Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Predictioncited by 8
  4. 2022Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinitycited by 7
  5. 2022SS-GNN: A Simple-Structured Graph Neural Network for Affinity Predictioncited by 7
  6. 2025SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learningcited by 5
  7. 2023GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity predictioncited by 4
  8. 2022Structure-based drug discovery with deep learningcited by 3
  9. 2024Binding Affinity Prediction: From Conventional to Machine Learning-Based Approachescited by 3
  10. 2023PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Mapscited by 2
  11. 2024Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Modelscited by 2
  12. 2025HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networkscited by 2
18 more
  1. 2021An Interpretable Framework for Drug-Target Interaction with Gated Cross Attentioncited by 1
  2. 2022GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRUcited by 1
  3. 2024Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspectivecited by 1
  4. 2025PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelscited by 1
  5. 2025DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representationcited by 1
  6. 2021CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Predictioncited by 0
  7. 2022Insights into performance evaluation of com-pound-protein interaction prediction methodscited by 0
  8. 2023ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connectionscited by 0
  9. 2023MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactionscited by 0
  10. 2023Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learningcited by 0
  11. 2024Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighborscited by 0
  12. 2025Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Predictioncited by 0
  13. 2026LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Predictioncited by 0
  14. 2026HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Predictioncited by 0
  15. 2026SPADE: Faster Drug Discovery by Learning from Sparse Datacited by 0
  16. 2026Chemical and geometric representation fidelity improves drug--target affinity predictioncited by 0
  17. 2026DPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Predictioncited by 0
  18. 2026Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Modelscited by 0