Prediction of Drug-target Binding Affinity by An Ensemble Lear ning System with Network Fusion Information

Title:Prediction of Drug-target Binding Affinity by An Ensemble Lear ning System with Network Fusion Information

Volume: 16 Issue: 10

Author(s): Cheng Lin Zhang, You Zhi Zhang*, Bing Wang*Peng Chen*

Affiliation:

• University Key Laboratory of Intelligent Perception and Computing of Anhui Province, School of Computer and Information, Anqing Normal University, 246133 Anqing, Anhui,China

• School of Electrical and Information Engineering, Anhui University of Technology, 243032 Ma'anshan, Anhui,China

• National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Institutes of Physical Science and Information Technology and School of Internet, Anhui University, 230601 Hefei, Anhui,China

Keywords: Binding affinity, drug-target interaction, network fusion, ensemble learning system, protein, computational.

Abstract:

Background: Verifying interactions between drugs and targets is key to discover new drugs. Many computational methods have been developed to predict drug-target interactions and performed successfully, but challenges still exist in the field.

Objective: We have made an attempt to develop a machine learning method to predict drug-target affinity, which can determine the strength of the binding relationship between drug and target.

Methods: This paper proposes an integrated machine learning system for drug-target binding affinity prediction based on network fusion. First, multiple similarity networks representing drugs or targets are calculated. Second, multiple networks representing drugs (targets) are fused separately. Finally, the characteristic information of splicing drugs and targets was used for model construction and training. By integrating multiple similarity networks, the model fully embodies the complementarity of network information, and the most complete features of information can be obtained after the redundancy is removed.

Results: Experimental results showed that our model obtained good results for DTI binding affinity.

Conclusion: It is still challenging to predict drug-target affinity. This paper proposes to use an integrated system of fusion network information for addressing the issue, and the proposed method performs well, which can provide a certain data basis for the subsequent work.

Cite this article as:

Zhang Lin Cheng, Zhang Zhi You*, Wang Bing*, Chen Peng *, Prediction of Drug-target Binding Affinity by An Ensemble Lear ning System with Network Fusion Information, Current Bioinformatics 2021; 16 (10) . https://dx.doi.org/10.2174/1574893616666210226114834