Predicting Drug-Target Interaction Using Deep Matrix Factorization

In silico prediction of drug-target interaction can help to speed up the process of identifying unknown interactions between drugs and target proteins in pharmaceutical research. In this paper, we first exploit k-nearest neighbor technique to identify the reliable negatives (non-interacting pairs) among unlabeled data. Then, we employ a Deep Matrix Factorization to predict drug-target interaction to reveal the non-linearity relations among interacting drugs and targets. We evaluate the results using area under the curve metrics. Our approach is applied to public-domain benchmarks and compared against the state-of-the-art methods.

[1]  Shu-Ching Chen,et al.  Correlation-Based Deep Learning for Multimedia Semantic Concept Detection , 2015, WISE.

[2]  Alexandros Karatzoglou,et al.  Deep Learning for Recommender Systems , 2017, RecSys.

[3]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[4]  Yoshihiro Yamanishi,et al.  Supervised prediction of drug–target interactions using bipartite local models , 2009, Bioinform..

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

[6]  Ping Zhang,et al.  Computational Drug Discovery with Dyadic Positive-Unlabeled Learning , 2017, SDM.

[7]  Shujian Huang,et al.  Deep Matrix Factorization Models for Recommender Systems , 2017, IJCAI.

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

[9]  Hao Ding,et al.  Similarity-based machine learning methods for predicting drug-target interactions: a brief review , 2014, Briefings Bioinform..

[10]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[11]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Yong Zhou,et al.  Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures , 2017, Molecules.

[13]  Anna Korhonen,et al.  Link prediction in drug-target interactions network using similarity indices , 2017, BMC Bioinformatics.

[14]  Daniel R. Caffrey,et al.  Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.

[15]  Mehmet Gönen,et al.  Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization , 2012, Bioinform..

[16]  Tao Chen,et al.  TriRank: Review-aware Explainable Recommendation by Modeling Aspects , 2015, CIKM.

[17]  Mathukumalli Vidyasagar,et al.  A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices , 2017, IEEE Transactions on Signal Processing.

[18]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[19]  Prasad Kulkarni,et al.  How Drugs are Developed and Approved by the FDA: Current Process and Future Directions , 2014, The American Journal of Gastroenterology.

[20]  Ali Masoudi-Nejad,et al.  Drug–target interaction prediction via chemogenomic space: learning-based methods , 2014, Expert opinion on drug metabolism & toxicology.

[21]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[22]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[23]  Xing Chen,et al.  In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences , 2017, Scientific Reports.

[24]  Yanli Wang,et al.  PubChem: Integrated Platform of Small Molecules and Biological Activities , 2008 .

[25]  Sang C. Suh,et al.  Integrative Gene Regulatory Network inference using multi-omics data , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[26]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[27]  Ming Gao,et al.  BiRank: Towards Ranking on Bipartite Graphs , 2017, IEEE Transactions on Knowledge and Data Engineering.

[28]  Louiqa Raschid,et al.  Ieee/acm Transactions on Computational Biology and Bioinformatics 1 Network-based Drug-target Interaction Prediction with Probabilistic Soft Logic , 2022 .

[29]  Salvatore Alaimo,et al.  Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning. , 2016, Methods in molecular biology.