GraphDTA: Predicting drug–target binding affinity with graph neural networks
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[1] Truyen Tran,et al. Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome , 2019, BMC Genomics.
[2] Fei Wang,et al. Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders , 2018, IJCAI.
[3] Maciej Eder,et al. Linguistic measures of chemical diversity and the “keywords” of molecular collections , 2018, Scientific Reports.
[4] Dong-Sheng Cao,et al. propy: a tool to generate various modes of Chou's PseAAC , 2013, Bioinform..
[5] Mathias Niepert,et al. Learning Convolutional Neural Networks for Graphs , 2016, ICML.
[6] Yi Xiong,et al. Predicting drug-target interactions using multi-label learning with community detection method (DTI-MLCD) , 2020, bioRxiv.
[7] Jonathan Masci,et al. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Elif Ozkirimli,et al. WideDTA: prediction of drug-target binding affinity , 2019, ArXiv.
[9] P. Cohen. Protein kinases — the major drug targets of the twenty-first century? , 2002, Nature reviews. Drug discovery.
[10] Mindy I. Davis,et al. Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.
[11] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[12] L. Johnson,et al. Protein Kinase Inhibitors: Insights into Drug Design from Structure , 2004, Science.
[13] Allen D. Roses,et al. Pharmacogenetics in drug discovery and development: a translational perspective , 2008, Nature Reviews Drug Discovery.
[14] Jiayu Zhou,et al. Graph convolutional networks for computational drug development and discovery , 2019, Briefings Bioinform..
[15] Jacob K. Asiedu,et al. The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.
[16] Juho Rousu,et al. Learning with multiple pairwise kernels for drug bioactivity prediction , 2018, Bioinform..
[17] Hugo Ceulemans,et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL† †Electronic supplementary information (ESI) available: Overview, Data Collection and Clustering, Methods, Results, Appendix. See DOI: 10.1039/c8sc00148k , 2018, Chemical science.
[18] M S Waterman,et al. Identification of common molecular subsequences. , 1981, Journal of molecular biology.
[19] Yi Xiong,et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features , 2019, Briefings Bioinform..
[20] Lei Jia,et al. Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction , 2018, International journal of molecular sciences.
[21] Tapio Pahikkala,et al. Toward more realistic drug^target interaction predictions , 2014 .
[22] T. Ashburn,et al. Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.
[23] Juho Rousu,et al. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors , 2017, PLoS Comput. Biol..
[24] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[25] Artem Cherkasov,et al. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines , 2017, Journal of Cheminformatics.
[26] Yutaka Saito,et al. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif , 2018, BMC Bioinformatics.
[27] T. Hunter,et al. The Protein Kinase Complement of the Human Genome , 2002, Science.
[28] Asher Mullard. New drugs cost US$2.6 billion to develop , 2014, Nature Reviews Drug Discovery.
[29] P. Bork,et al. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. , 2012, Current opinion in biotechnology.
[30] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[31] Yiqun Cao,et al. ChemMine tools: an online service for analyzing and clustering small molecules , 2011, Nucleic Acids Res..
[32] Chong Wang,et al. Attention-based Graph Neural Network for Semi-supervised Learning , 2018, ArXiv.
[33] Ping Zhang,et al. Interpretable Drug Target Prediction Using Deep Neural Representation , 2018, IJCAI.
[34] George Karypis,et al. Frequent substructure-based approaches for classifying chemical compounds , 2003, IEEE Transactions on Knowledge and Data Engineering.
[35] Le Zhang,et al. An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking , 2019, Interdisciplinary Sciences: Computational Life Sciences.
[36] Amos Bairoch,et al. PROSITE, a protein domain database for functional characterization and annotation , 2009, Nucleic Acids Res..
[37] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[38] Arzucan Özgür,et al. DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..
[39] S. Strittmatter,et al. Overcoming Drug Development Bottlenecks With Repurposing: Old drugs learn new tricks , 2014, Nature Medicine.
[40] Philip E. Bourne,et al. A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing , 2011, J. Chem. Inf. Model..
[41] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[42] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[43] Artem Cherkasov,et al. PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction , 2018, ArXiv.
[44] Russ B. Altman,et al. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions , 2018, bioRxiv.
[45] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[46] Trey Ideker,et al. A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing , 2020, bioRxiv.
[47] Paul Zuck,et al. Maximizing diversity from a kinase screen: identification of novel and selective pan-Trk inhibitors for chronic pain. , 2014, Journal of medicinal chemistry.
[48] Zhengyang Wang,et al. Large-Scale Learnable Graph Convolutional Networks , 2018, KDD.
[49] Jure Leskovec,et al. Modeling polypharmacy side effects with graph convolutional networks , 2018, bioRxiv.
[50] Andreas Zell,et al. Feature Selection for Descriptor Based Classification Models. 2. Human Intestinal Absorption (HIA) , 2004, J. Chem. Inf. Model..
[51] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[52] Russ B Altman,et al. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions , 2019, J. Chem. Inf. Model..
[53] Tao Xu,et al. Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis , 2014, J. Chem. Inf. Model..