# Interpretable bilinear attention network with domain adaptation improves drug-target prediction

arXiv: https://arxiv.org/abs/2208.02194 · HTML: https://mlsanity.com/paper/2208.02194/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering](https://mlsanity.com/paper/1708.03619/) (2017)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities](https://mlsanity.com/paper/1807.00123/) (2018)
- [DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science](https://mlsanity.com/paper/2106.14232/) (2021)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction](https://mlsanity.com/paper/1807.09741/) (2018)
- [Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning](https://mlsanity.com/paper/2107.00719/) (2021)

## Built on by (most cited first, 30 of 39)

- [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://mlsanity.com/paper/2310.07276/) (2023)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning](https://mlsanity.com/paper/2402.17810/) (2024)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2405.14545/) (2024)
- [Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.08975/) (2025)
- [ASI-Evolve: AI Accelerates AI](https://mlsanity.com/paper/2603.29640/) (2026)
- [Towards a more inductive world for drug repurposing approaches](https://mlsanity.com/paper/2311.12670/) (2023)
- [Regressor-free Molecule Generation to Support Drug Response Prediction](https://mlsanity.com/paper/2405.14536/) (2024)
- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
- [Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development](https://mlsanity.com/paper/2410.11550/) (2024)
- [FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning](https://mlsanity.com/paper/2410.15010/) (2024)
- [Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model](https://mlsanity.com/paper/2403.13244/) (2024)
- [HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2404.10561/) (2024)
- [Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries](https://mlsanity.com/paper/2409.05916/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks](https://mlsanity.com/paper/2504.02014/) (2025)
- [Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction](https://mlsanity.com/paper/2507.20925/) (2025)
- [Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening](https://mlsanity.com/paper/2310.12996/) (2023)
- [CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision](https://mlsanity.com/paper/2312.10707/) (2023)
- [Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective](https://mlsanity.com/paper/2407.04055/) (2024)
- [Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction](https://mlsanity.com/paper/2501.16391/) (2025)
- [Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning](https://mlsanity.com/paper/2502.12049/) (2025)
- [Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction](https://mlsanity.com/paper/2408.00040/) (2024)
- [Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration](https://mlsanity.com/paper/2502.01461/) (2025)
- [Partial Distribution Alignment via Adaptive Optimal Transport](https://mlsanity.com/paper/2503.05087/) (2025)
- [SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction](https://mlsanity.com/paper/2503.09251/) (2025)
- [Heterogeneous networks in drug-target interaction prediction](https://mlsanity.com/paper/2504.16152/) (2025)
- [ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models](https://mlsanity.com/paper/2506.00880/) (2025)
- [KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2506.13196/) (2025)

## Abstract (arXiv)

Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution. In this work, we propose DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pair-wise local interactions between drugs and targets, and adapt on out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug-target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baselines. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.