mlsanityData to 14 Oct 2026

Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction

arXiv 1908.06760 ↗First posted 15 Aug 2019cs.LG stat.MLcited by 22

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Abstract (arXiv)

Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Aug 2019.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

22 papers.

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  1. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 206
  2. 2021Geometric Deep Learning on Molecular Representationscited by 113
  3. 2021Chemical-Reaction-Aware Molecule Representation Learningcited by 36
  4. 2022In Defense of Core-set: A Density-aware Core-set Selection for Active Learningcited by 27
  5. 2021Improving evidential deep learning via multi-task learningcited by 13
  6. 2022Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinitycited by 7
  7. 2023Difficulty in chirality recognition for Transformer architectures learning chemical structures from stringcited by 7
  8. 2023ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervisioncited by 7
  9. 2024ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modelingcited by 7
  10. 2024A Cross-Field Fusion Strategy for Drug-Target Interaction Predictioncited by 5
  11. 2021Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learningcited by 4
  12. 2022Structure-based drug discovery with deep learningcited by 3
10 more
  1. 2023DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanismscited by 2
  2. 2024Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer propertiescited by 2
  3. 2025Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Reviewcited by 2
  4. 2021An Interpretable Framework for Drug-Target Interaction with Gated Cross Attentioncited by 1
  5. 2022GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRUcited by 1
  6. 2026A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Eracited by 1
  7. 2021Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discoverycited by 0
  8. 2022Quality-diversity in dissimilarity spacescited by 0
  9. 2025Flow-Based Fragment Identification via Binding Site-Specific Latent Representationscited by 0
  10. 2025GRAM-DTI: adaptive multimodal representation learning for drug target interaction predictioncited by 0