# An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention

arXiv: https://arxiv.org/abs/2109.08360 · HTML: https://mlsanity.com/paper/2109.08360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification](https://mlsanity.com/paper/1602.02068/) (2016)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Quasi-Recurrent Neural Networks](https://mlsanity.com/paper/1611.01576/) (2016)
- [Natural Language Inference over Interaction Space](https://mlsanity.com/paper/1709.04348/) (2017)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [Attentive Pooling Networks](https://mlsanity.com/paper/1602.03609/) (2016)
- [Towards Better Interpretability in Deep Q-Networks](https://mlsanity.com/paper/1809.05630/) (2018)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling](https://mlsanity.com/paper/1801.10296/) (2018)
- [Are deep ResNets provably better than linear predictors?](https://mlsanity.com/paper/1907.03922/) (2019)
- [Controlled Molecule Generator for Optimizing Multiple Chemical Properties](https://mlsanity.com/paper/2010.13908/) (2020)

## Built on by (most cited first, 1 of 1)

- [Enhancing Multilingual Reasoning via Steerable Model Merging](https://mlsanity.com/paper/2606.19002/) (2026)

## Abstract (arXiv)

In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process. Specifically, deep learning-based DTI approaches have been shown promising results in terms of accuracy and low cost for the prediction. However, they pay little attention to the interpretability of their prediction results and feature-level interactions between a drug and a target. In this study, we propose a novel interpretable framework that can provide reasonable cues for the interaction sites. To this end, we elaborately design a gated cross-attention mechanism that crossly attends drug and target features by constructing explicit interactions between these features. The gating function in the method enables neural models to focus on salient regions over entire sequences of drugs and proteins, and the byproduct from the function, which is the attention map, could serve as interpretable factors. The experimental results show the efficacy of the proposed method in two DTI datasets. Additionally, we show that gated cross-attention can sensitively react to the mutation, and this result could provide insights into the identification of novel drugs targeting mutant proteins.