Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Author(s):  
Zhirui Liao ◽  
Xiaodi Huang ◽  
Hiroshi Mamitsuka ◽  
Shanfeng Zhu
2020 ◽  
Vol 11 (9) ◽  
pp. 2531-2557 ◽  
Author(s):  
Ahmet Sureyya Rifaioglu ◽  
Esra Nalbat ◽  
Volkan Atalay ◽  
Maria Jesus Martin ◽  
Rengul Cetin-Atalay ◽  
...  

The DEEPScreen system is composed of 704 target protein specific prediction models, each independently trained using experimental bioactivity measurements against many drug candidate small molecules, and optimized according to the binding properties of the target proteins.


Author(s):  
Ali Ezzat ◽  
Peilin Zhao ◽  
Min Wu ◽  
Xiao-Li Li ◽  
Chee-Keong Kwoh

2020 ◽  
Author(s):  
Ming Chen ◽  
Xiuze Zhou

Abstract Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development. Results: For drug-target interaction prediction, we propose a novel neural network architecture, DAEi, extended from Denoising AutoEncoder (DAE). We assume that a set of verified DTIs is a corrupted version of the full interaction set. We use DAEi to learn latent features from corrupted DTIs to reconstruct the full input. Also, to better predict DTIs, we add some similarities to DAEi and adopt a new nonlinear method for calculation. Similarity information is very effective at improving the prediction of DTIs. Conclusion: Results of the extensive experiments we conducted on four real data sets show that our proposed methods are superior to other baseline approaches.Availability: All codes in this paper are open-sourced, and our projects are available at: https://github.com/XiuzeZhou/DAEi.


2016 ◽  
Vol 32 (12) ◽  
pp. i18-i27 ◽  
Author(s):  
Qingjun Yuan ◽  
Junning Gao ◽  
Dongliang Wu ◽  
Shihua Zhang ◽  
Hiroshi Mamitsuka ◽  
...  

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