MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities

By Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, Jianyang Zeng. 2020

215 citations recorded by the source corpus.

Summary Computational approaches for understanding compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, a number of deep-learning-based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions (i.e., neural network architectures that enable the interpretation of feature importance). Here, we compiled a benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs and systematically evaluated the interpretability of neural attentions in existing models. We also developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinities between compounds and proteins. Comprehensive evaluation demonstrated that MONN can successfully predict the non-covalent interactions between compounds and proteins that cannot be effectively captured by neural attentions in previous prediction methods. Moreover, MONN outperforms other state-of-the-art methods in predicting binding affinities. Source code for MONN is freely available for download at https://github.com/lishuya17/MONN .

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jianzhu Ma
2. Jian Peng
3. Jiaqi Guan
4. Xingang Peng
5. Sheng Wang
6. Jinbo Xu

In Materials Chemistry

1. Jianzhu Ma
2. Jian Peng
3. Wesley Wei Qian
4. Jiaqi Guan
5. Xingang Peng
6. Yufeng Su
7. Jinbo Xu
8. Sheng Wang

In Molecular Biology

1. Jianzhu Ma
2. Jian Peng
3. Mingjing Tang
4. Wesley Wei Qian
5. Jiaqi Guan
6. Xiao-Meng Zhang
7. Lin Liu
8. Li Liang

Citations in the corpus

• Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “…retrieval (Zhai et al., 2019), brain connection (Ktena et al., 2017, 2018; Li X. et al., 2019a; Mirakhorli and Mirakhorli, 2019; Grigis et al., 2020; Li et al., 2020; Zhang and Pierre, 2020; Zhang et al., 2021) and disease prediction (Parisot et al., 2017; Kazi et al., 2018, 2019a,b,c; Anirudh…”
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “We augment EGNN (Satorras et al., 2021b) with the unsupervised features h L provided by our model, and compare it with two state-of-the-art sequence-based models TransCPI (Chen et al., 2020) and MONN (Li et al., 2020), one complex model IGN (Jiang et al., 2021), two structure-based model HOLOPROT (S”