From chemoinformatics to deep learning: an open road to drug discovery

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[2] [Anonymous], 2015, DEEP LEARNING NATURE, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]

[3] Application of Generative Autoencoder in De Novo Molecular Design [J]. MOLECULAR INFORMATICS, 2018, 37 (1-2)

[4] The rise of deep learning in drug discovery [J]. DRUG DISCOVERY TODAY, 2018, 23 (06) : 1241 - 1250

[5] Prediction of Organic Reaction Outcomes Using Machine Learning [J]. ACS CENTRAL SCIENCE, 2017, 3 (05) : 434 - 443

[6] Dahl G. E., 2014, Multi-task neural networks for qsar predictions

[7] Efficient codes and balanced networks [J]. NATURE NEUROSCIENCE, 2016, 19 (03) : 375 - 382

[8] Evolutionary artificial neural networks: a review [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2013, 39 (03) : 251 - 260

[9] Fleming N, 2018, NATURE, V557, pS55, DOI 10.1038/d41586-018-05267-x

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[1] Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data [J]. MOLECULAR PHARMACEUTICS, 2016, 13 (07) : 2524 - 2530

[2] [Anonymous], 2015, DEEP LEARNING NATURE, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]

[3] Application of Generative Autoencoder in De Novo Molecular Design [J]. MOLECULAR INFORMATICS, 2018, 37 (1-2)

[4] The rise of deep learning in drug discovery [J]. DRUG DISCOVERY TODAY, 2018, 23 (06) : 1241 - 1250

[5] Prediction of Organic Reaction Outcomes Using Machine Learning [J]. ACS CENTRAL SCIENCE, 2017, 3 (05) : 434 - 443

[6] Dahl G. E., 2014, Multi-task neural networks for qsar predictions

[7] Efficient codes and balanced networks [J]. NATURE NEUROSCIENCE, 2016, 19 (03) : 375 - 382

[8] Evolutionary artificial neural networks: a review [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2013, 39 (03) : 251 - 260

[9] Fleming N, 2018, NATURE, V557, pS55, DOI 10.1038/d41586-018-05267-x

[10] Advancing drug discovery via GPU-based deep learning [J]. EXPERT OPINION ON DRUG DISCOVERY, 2018, 13 (07) : 579 - 582