Drug-target Interaction Prediction via Multiple Output Deep Learning

Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. However, by connecting drugs and targets to form drug target pairs, the number of interactions is limit, most interactions focus on only a few targets or a few drugs, and the number of drug target pairs is far more than the number of interactions, which causes to be over fitting. To overcome the above problem, in this paper, a multiple output deep neural network (MODNN) based DTI prediction is designed. MODNN enhances its learning ability with a kind of auxiliary classifier layers. The parameters used in the training process are elaborated from the auxiliary and main classifier layers, which can increase the gradient signal that gets propagated back, utilize multi-level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The conducted experiments validate the effectiveness of our MODNN.

[1]  Z. R. Li,et al.  PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks. , 2017, Journal of molecular biology.

[2]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[3]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[4]  Yusuke Nakashima,et al.  CoDe-DTI: Collaborative Deep Learning-based Drug-Target Interaction Prediction , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[6]  Hanbi Lee,et al.  Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data , 2019, Pharmaceutics.

[7]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[8]  Huiyou Chang,et al.  Predicting Drug-target Interaction via Wide and Deep Learning , 2018, ICBCB 2018.

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Hojung Nam,et al.  DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences , 2018, PLoS Comput. Biol..

[11]  Ming Wen,et al.  Deep-Learning-Based Drug-Target Interaction Prediction. , 2017, Journal of proteome research.

[12]  Jingjing Wang,et al.  A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder , 2020, Frontiers in Pharmacology.

[13]  Elena Marchiori,et al.  Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..

[14]  Pingzhao Hu,et al.  Predicting drug-target interaction network using deep learning model , 2019, Comput. Biol. Chem..

[15]  Dong-Qing Wei,et al.  SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction , 2020, Frontiers in Chemistry.

[16]  E. Marchiori,et al.  Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile , 2013, PloS one.

[17]  CHUN WEI YAP,et al.  PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..

[18]  Song He,et al.  Deep learning-based transcriptome data classification for drug-target interaction prediction , 2018, BMC Genomics.

[19]  Yasuo Tabei,et al.  Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers , 2012, Bioinform..

[20]  Min Wu,et al.  Drug-target interaction prediction using ensemble learning and dimensionality reduction. , 2017, Methods.

[21]  Thomas J. Ashby,et al.  Industry-scale application and evaluation of deep learning for drug target prediction , 2020, Journal of Cheminformatics.

[22]  Xin Xu,et al.  Efficient Classification of Hot Spots and Hub Protein Interfaces by Recursive Feature Elimination and Gradient Boosting , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  Xiaolong Zhang,et al.  Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.