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Modeling Diverse Chemical Reactions for Single-step Retrosy..:
, In:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
,
He, Hua-Rui
;
Wang, Jie
;
Liu, Yunfei
. - p. 717-726 , 2022
Link:
https://dl.acm.org/doi/10.1145/3511808.3557397
RT T1 <cTitle>Proceedings of the 31st ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3557397&Exemplar=1&LAN=DE A1 He, Hua-Rui A1 Wang, Jie A1 Liu, Yunfei A1 Wu, Feng PB ACM YR 2022 K1 discrete latent variable K1 graph neural network K1 retrosynthesis K1 transformer K1 variational autoencoder K1 Applied computing K1 Life and medical sciences K1 Computational biology K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Learning in probabilistic graphical models K1 Latent variable models K1 Physical sciences and engineering K1 Chemistry K1 Artificial intelligence K1 Natural language processing SP 717 OP 726 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3511808.3557397 DO https://dl.acm.org/doi/10.1145/3511808.3557397 SF ELIB - SuUB Bremen
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