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Learning multimodal graph-to-graph translation for molecular optimization. arXiv preprint arXiv:1812.01070 (2018)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Sunghwan Kim Paul A Thiessen Evan E Bolton Jie Chen Gang Fu Asta Gindulyte Lianyi Han Jane He Siqian He Benjamin A Shoemaker etal 2016. PubChem substance and compound databases. Nucleic acids research Vol. 44 D1 (2016) D1202--D1213.  Sunghwan Kim Paul A Thiessen Evan E Bolton Jie Chen Gang Fu Asta Gindulyte Lianyi Han Jane He Siqian He Benjamin A Shoemaker et al. 2016. PubChem substance and compound databases. Nucleic acids research Vol. 44 D1 (2016) D1202--D1213.","DOI":"10.1093\/nar\/gkv951"},{"key":"e_1_3_2_2_33_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). 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Nucleic acids research, Vol. 35, suppl_1 (2007), D198--D201."},{"key":"e_1_3_2_2_42_1","first-page":"6229","article-title":"A 3D generative model for structure-based drug design","volume":"34","author":"Luo Shitong","year":"2021","unstructured":"Shitong Luo , Jiaqi Guan , Jianzhu Ma , and Jian Peng . 2021 . A 3D generative model for structure-based drug design . Advances in Neural Information Processing Systems , Vol. 34 (2021), 6229 -- 6239 . Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng. 2021. A 3D generative model for structure-based drug design. Advances in Neural Information Processing Systems, Vol. 34 (2021), 6229--6239.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"Identification of Enzymatic Active Sites with Unsupervised Language Modeling. 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DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures. (2018).  Noel O'Boyle and Andrew Dalke. 2018. DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures. (2018).","DOI":"10.26434\/chemrxiv.7097960"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0235-x"},{"key":"e_1_3_2_2_50_1","volume-title":"How many drug targets are there? Nature reviews Drug discovery","author":"Overington John P","year":"2006","unstructured":"John P Overington , Bissan Al-Lazikani , and Andrew L Hopkins . 2006. How many drug targets are there? Nature reviews Drug discovery , Vol. 5 , 12 ( 2006 ), 993--996. John P Overington, Bissan Al-Lazikani, and Andrew L Hopkins. 2006. How many drug targets are there? Nature reviews Drug discovery, Vol. 5, 12 (2006), 993--996."},{"key":"e_1_3_2_2_51_1","volume-title":"Deep reinforcement learning for de novo drug design. 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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)."},{"key":"e_1_3_2_2_53_1","volume-title":"Evaluating protein transfer learning with TAPE. Advances in neural information processing systems","author":"Rao Roshan","year":"2019","unstructured":"Roshan Rao , Nicholas Bhattacharya , Neil Thomas , Yan Duan , Peter Chen , John Canny , Pieter Abbeel , and Yun Song . 2019. Evaluating protein transfer learning with TAPE. Advances in neural information processing systems , Vol. 32 ( 2019 ). Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. 2019. Evaluating protein transfer learning with TAPE. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1101\/2021.02.12.430858"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_2_2_56_1","volume-title":"Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology","author":"Schisterman Enrique F","year":"2005","unstructured":"Enrique F Schisterman , Neil J Perkins , Aiyi Liu , and Howard Bondell . 2005. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology ( 2005 ), 73--81. Enrique F Schisterman, Neil J Perkins, Aiyi Liu, and Howard Bondell. 2005. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. 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Advances in Neural Information Processing Systems, Vol. 34 (2021), 7924--7936.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_64_1","volume-title":"Graph convolutional policy network for goal-directed molecular graph generation. Advances in neural information processing systems","author":"You Jiaxuan","year":"2018","unstructured":"Jiaxuan You , Bowen Liu , Zhitao Ying , Vijay Pande , and Jure Leskovec . 2018. Graph convolutional policy network for goal-directed molecular graph generation. Advances in neural information processing systems , Vol. 31 ( 2018 ). Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec. 2018. Graph convolutional policy network for goal-directed molecular graph generation. 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