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

arXiv: https://arxiv.org/abs/1908.06760 · HTML: https://mlsanity.com/paper/1908.06760/

## Key facts

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

## Built on by (most cited first, 22 of 22)

- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
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- [Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity](https://mlsanity.com/paper/2201.06872/) (2022)
- [Difficulty in chirality recognition for Transformer architectures learning chemical structures from string](https://mlsanity.com/paper/2303.11593/) (2023)
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- [ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling](https://mlsanity.com/paper/2403.12995/) (2024)
- [A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2405.14545/) (2024)
- [Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning](https://mlsanity.com/paper/2107.00719/) (2021)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms](https://mlsanity.com/paper/2312.15252/) (2023)
- [Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties](https://mlsanity.com/paper/2407.02770/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (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)
- [A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era](https://mlsanity.com/paper/2604.16586/) (2026)
- [Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery](https://mlsanity.com/paper/2104.11674/) (2021)
- [Quality-diversity in dissimilarity spaces](https://mlsanity.com/paper/2211.12337/) (2022)
- [Flow-Based Fragment Identification via Binding Site-Specific Latent Representations](https://mlsanity.com/paper/2509.13216/) (2025)
- [GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction](https://mlsanity.com/paper/2509.21971/) (2025)

## 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.