# Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations

arXiv: https://arxiv.org/abs/2302.00981 · HTML: https://mlsanity.com/paper/2302.00981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 63 references that are in the map.
- First posted on arXiv 2 Feb 2023.

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

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- [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://mlsanity.com/paper/1906.05017/) (2019)
- [PotentialNet for Molecular Property Prediction](https://mlsanity.com/paper/1803.04465/) (2018)
- [Chemi-net: a graph convolutional network for accurate drug property prediction](https://mlsanity.com/paper/1803.06236/) (2018)
- [Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations](https://mlsanity.com/paper/2102.04064/) (2021)
- [Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction](https://mlsanity.com/paper/2102.01649/) (2021)

## Abstract (arXiv)

The study of molecule-target interaction is quite important for drug discovery in terms of target identification, hit identification, pathway study, drug-drug interaction, etc. Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link. However, the biomedical network information based methods usually suffer from cold start problem, while structure based methods often give limited performance due to the structure/interaction assumption and data quality. To address these issues, we propose a pseudo-siamese Graph Neural Network method, namely MTINet+, which learns both biomedical network topological and molecule structural/chemical information as representations to predict potential interaction of given molecule and target pair. In MTINet+, 1-hop subgraphs of given molecule and target pair are extracted from known interaction of biomedical network as topological information, meanwhile the molecule structural and chemical attributes are processed as molecule information. MTINet+ learns these two types of information as embedding features for predicting the pair link. In the experiments of different molecule-target interaction tasks, MTINet+ significantly outperforms over the state-of-the-art baselines. In addition, in our designed network sparsity experiments , MTINet+ shows strong robustness against different sparse biomedical networks.