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引用“”的文章The rise of deep learning in drug discovery

标题/作者	年份
Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions L Alzubaidi, J Zhang, AJ Humaidi, A Al-Dujaili, Y Duan, O Al-Shamma, ... Journal of big Data 8, 1-74	2021
Applications of machine learning in drug discovery and development J Vamathevan, D Clark, P Czodrowski, I Dunham, E Ferran, G Lee, B Li, ... Nature reviews Drug discovery 18 (6), 463-477	2019
Junction tree variational autoencoder for molecular graph generation W Jin, R Barzilay, T Jaakkola International conference on machine learning, 2323-2332	2018
Machine learning in chemoinformatics and drug discovery YC Lo, SE Rensi, W Torng, RB Altman Drug discovery today 23 (8), 1538-1546	2018
GuacaMol: benchmarking models for de novo molecular design N Brown, M Fiscato, MHS Segler, AC Vaucher Journal of chemical information and modeling 59 (3), 1096-1108	2019
Concepts of artificial intelligence for computer-assisted drug discovery X Yang, Y Wang, R Byrne, G Schneider, S Yang Chemical reviews 119 (18), 10520-10594	2019
Artificial intelligence in drug development: present status and future prospects KK Mak, MR Pichika Drug discovery today 24 (3), 773-780	2019
Expanding the medicinal chemistry synthetic toolbox J Boström, DG Brown, RJ Young, GM Keserü Nature Reviews Drug Discovery 17 (10), 709-727	2018
Drug discovery with explainable artificial intelligence J Jiménez-Luna, F Grisoni, G Schneider Nature Machine Intelligence 2 (10), 573-584	2020
Deep learning in chemistry AC Mater, ML Coote Journal of chemical information and modeling 59 (6), 2545-2559	2019
Self-supervised graph transformer on large-scale molecular data Y Rong, Y Bian, T Xu, W Xie, Y Wei, W Huang, J Huang Advances in Neural Information Processing Systems 33, 12559-12571	2020
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning AF Zahrt, JJ Henle, BT Rose, Y Wang, WT Darrow, SE Denmark Science 363 (6424), eaau5631	2019
Exploiting machine learning for end-to-end drug discovery and development S Ekins, AC Puhl, KM Zorn, TR Lane, DP Russo, JJ Klein, AJ Hickey, ... Nature materials 18 (5), 435-441	2019
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations R Winter, F Montanari, F Noé, DA Clevert Chemical science 10 (6), 1692-1701	2019
The importance of interpretability and visualization in machine learning for applications in medicine and health care A Vellido Neural computing and applications 32 (24), 18069-18083	2020
Artificial intelligence to deep learning: machine intelligence approach for drug discovery R Gupta, D Srivastava, M Sahu, S Tiwari, RK Ambasta, P Kumar Molecular diversity 25, 1315-1360	2021
Graph convolutional networks for computational drug development and discovery M Sun, S Zhao, C Gilvary, O Elemento, J Zhou, F Wang Briefings in bioinformatics 21 (3), 919-935	2020
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models D Jiang, Z Wu, CY Hsieh, G Chen, B Liao, Z Wang, C Shen, D Cao, J Wu, ... Journal of cheminformatics 13 (1), 1-23	2021
Artificial intelligence in drug design G Hessler, KH Baringhaus Molecules 23 (10), 2520	2018
Molecular representations in AI-driven drug discovery: a review and practical guide L David, A Thakkar, R Mercado, O Engkvist Journal of Cheminformatics 12 (1), 1-22	2020

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