Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope
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[1] Sereina Riniker,et al. Open-source platform to benchmark fingerprints for ligand-based virtual screening , 2013, Journal of Cheminformatics.
[2] Jijun Tang,et al. Identification of drug-target interactions via multiple information integration , 2017, Inf. Sci..
[3] Antonio Peón,et al. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space , 2017, Scientific Reports.
[4] Nils-Ole Friedrich,et al. Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters , 2018, ChemMedChem.
[5] Petra Schneider,et al. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus , 2014, Proceedings of the National Academy of Sciences.
[6] Yanjie Wei,et al. DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity , 2019, PeerJ.
[7] Michael J. Keiser,et al. Predicting new molecular targets for known drugs , 2009, Nature.
[8] Hugo Ceulemans,et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL , 2018, Chemical science.
[9] Chee-Keong Kwoh,et al. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey , 2019, Briefings Bioinform..
[10] 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.
[11] Adrià Cereto-Massagué,et al. Tools for in silico target fishing. , 2015, Methods.
[12] Bernardete Ribeiro,et al. Deep Neural Network Architecture for Drug-Target Interaction Prediction , 2019, ICANN.
[13] Antonio Lavecchia,et al. In silico methods to address polypharmacology: current status, applications and future perspectives. , 2016, Drug discovery today.
[14] Jean-Louis Reymond,et al. Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning , 2018, J. Chem. Inf. Model..
[15] Lukasz Kurgan,et al. Review and comparative assessment of similarity-based methods for prediction of drug-protein interactions in the druggable human proteome , 2018, Briefings Bioinform..
[16] Michael J. Keiser,et al. Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.
[17] Aurélien Grosdidier,et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules , 2014, Nucleic Acids Res..
[18] Shuxing Zhang,et al. Computational polypharmacology: a new paradigm for drug discovery , 2017, Expert opinion on drug discovery.
[19] Antonino Lauria,et al. Drugs Polypharmacology by In Silico Methods: New Opportunities in Drug Discovery. , 2016, Current pharmaceutical design.
[20] Hao Ding,et al. Similarity-based machine learning methods for predicting drug-target interactions: a brief review , 2014, Briefings Bioinform..
[21] G. Schneider,et al. Rethinking drug design in the artificial intelligence era , 2019, Nature Reviews Drug Discovery.
[22] Hojung Nam,et al. SELF-BLM: Prediction of drug-target interactions via self-training SVM , 2017, PloS one.
[23] Isidro Cortes-Ciriano,et al. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects , 2015 .
[24] Lirong Wang,et al. TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database , 2013, The AAPS Journal.
[25] Diego di Bernardo,et al. Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis , 2014, Bioinform..
[26] Andrew R. Leach,et al. Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery , 2019, Journal of Cheminformatics.
[27] Lukasz Kurgan,et al. Survey of Similarity-based Prediction of Drug-protein Interactions. , 2018, Current medicinal chemistry.
[28] Xin Geng,et al. Binary relevance for multi-label learning: an overview , 2018, Frontiers of Computer Science.
[29] Olivier Taboureau,et al. Network‐based Approaches in Pharmacology , 2017, Molecular informatics.
[30] Dongsup Kim,et al. In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning , 2019, Genes.
[31] Xiaolin Cheng,et al. STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products , 2019, J. Chem. Inf. Model..
[32] Robert Damoiseaux,et al. 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping. , 2016, ACS chemical biology.
[33] Ewgenij Proschak,et al. Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds. , 2018, Journal of medicinal chemistry.
[34] M. Prunotto,et al. Opportunities and challenges in phenotypic drug discovery: an industry perspective , 2017, Nature Reviews Drug Discovery.
[35] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[36] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[37] Xiaofeng Liu,et al. ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method , 2013, Bioinform..
[38] Bin Chen,et al. Predicting drug target interactions using meta-path-based semantic network analysis , 2016, BMC Bioinformatics.
[39] Gisbert Schneider,et al. Deep Learning in Drug Discovery , 2016, Molecular informatics.
[40] Yi Xiong,et al. DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features , 2019, bioRxiv.
[41] Ya Chen,et al. Validation strategies for target prediction methods , 2019, Briefings Bioinform..
[42] G. Schneider,et al. Active learning for computational chemogenomics. , 2017, Future medicinal chemistry.
[43] Shuxing Zhang,et al. Polypharmacology: drug discovery for the future , 2013, Expert review of clinical pharmacology.
[44] George Papadatos,et al. The ChEMBL database in 2017 , 2016, Nucleic Acids Res..
[45] 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..
[46] T. Rodrigues,et al. Machine learning for target discovery in drug development. , 2019, Current opinion in chemical biology.
[47] Bin Yu,et al. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. , 2019, Genomics.
[48] Yanli Wang,et al. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions , 2019, Briefings Bioinform..
[49] Kwong-Sak Leung,et al. MolTarPred: A web tool for comprehensive target prediction with reliability estimation , 2019, Chemical biology & drug design.
[50] Andrea Volkamer,et al. Advances and Challenges in Computational Target Prediction , 2019, J. Chem. Inf. Model..
[51] Mathias Dunkel,et al. SuperPred: update on drug classification and target prediction , 2014, Nucleic Acids Res..
[52] Michael J. Keiser,et al. Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.