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Molecular representation learning plays an essential role in cheminformatics.
Strategies for pre-training graph neural networks
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Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
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The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
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Molecular identification number for substructure searches
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A guide to IUPAC Nomenclature of Organic Compounds , volume 2
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Molecular similarity: a key technique in molecular informatics
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Computer Aided Property Estimation for Process and Product Design: Computers Aided Chemical Engineering
Georgios M Kontogeorgis and Rafiqul Gani. 2004 · 2004
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Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al. 2020 · 2007
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Bert learns (and teaches) chemistry
Josh Payne, Mario Srouji, Dian Ang Yap, and Vineet Kosaraju. 2020 · 2007
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020 · 2007
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Contrastive visual-linguistic pretraining
Lei Shi, Kai Shuang, Shijie Geng, Peng Su, Zhengkai Jiang, Peng Gao, Zuohui Fu, Gerard de Melo, and Sen Su. 2020 · 2007
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Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints
Martin Vogt and Jürgen Bajorath. 2008 · 2008
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Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. 2020 · 2010
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Unsupervised natural language inference via decoupled multimodal contrastive learning
Wanyun Cui, Guangyu Zheng, and Wei Wang. 2020 · 2010
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn. 2010 · 2010
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Scikit-learn: Machine learning in Python
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Parameter efficient multimodal transformers for video representation learning
Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, and Yale Song. 2020 · 2012
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P4contrast: Contrastive learning with pairs of point-pixel pairs for rgb-d scene understanding
Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, and Hao Dong. 2020 · 2012
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Nomenclature of organic chemistry: IUPAC recommendations and preferred names 2013
Henri A Favre and Warren H Powell. 2013 · 2013
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum. 2013 · 2013
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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PubChem substance and compound databases
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 · 2016
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An overview of molecular fingerprint similarity search in virtual screening
Ingo Muegge and Prasenjit Mukherjee. 2016 · 2016
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Semantic re-tuning with contrastive tension
Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, and Magnus Sahlgren. 2020 · 2020
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Dpddi: a deep predictor for drug-drug interactions
Yue-Hua Feng, Shao-Wu Zhang, and Jian-Yu Shi. 2020 · 2020
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Improving unimodal object recognition with multimodal contrastive learning
Johannes Meyer, Andreas Eitel, Thomas Brox, and Wolfram Burgard. 2020 · 2020
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Vae-sim: a novel molecular similarity measure based on a variational autoencoder
Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J Roberts, and Douglas B Kell. 2020 · 2020
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X-mol: large-scale pre-training for molecular understanding and diverse molecular analysis
Dongyu Xue, Han Zhang, Dongling Xiao, Yukang Gong, Guohui Chuai, Yu Sun, Hao Tian, Hua Wu, Yukun Li, and Qi Liu. 2021 · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Garrett B Goh, Nathan O Hodas, Charles Siegel, and Abhinav Vishnu. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Zheng Xu, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2017 · 2017
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Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, and Xiaohong Li. 2017 · 2017
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Latent molecular optimization for targeted therapeutic design
Tristan Aumentado-Armstrong. 2018 · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik. 2018 · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville. 2018 · 2018
Cited alongside, same era.
Hassan Akbari, Linagzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. 2021 · 2021
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Liggpt: Molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar. 2021 · 2021
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Multimodal clustering networks for self-supervised learning from unlabeled videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte, Hilde Kuehne, Samuel Thomas, Angie Boggust, Rameswar Panda, Brian Kingsbury, Rogerio Feris, David Harwath, et al. 2021 · 2021
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Translating the molecules: adapting neural machine translation to predict iupac names from a chemical identifier
Jennifer Handsel, Brian Matthews, Nicola Knight, and Simon Coles. 2021 · 2021
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Embedding of molecular structure using molecular hypergraph variational autoencoder with metric learning
Daiki Koge, Naoaki Ono, Ming Huang, Md Altaf-Ul-Amin, and Shigehiko Kanaya. 2021 · 2021
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Struct2iupac–transformer-based artificial neural network for the conversion between chemical notations
Lev Krasnov, Ivan Khokhlov, Maxim Fedorov, and Sergey Sosnin. 2021 · 2021
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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
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Stout: Smiles to iupac names using neural machine translation
Kohulan Rajan, Achim Zielesny, and Christoph Steinbeck. 2021 · 2021
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Mapping the space of chemical reactions using attention-based neural networks
Philippe Schwaller, Daniel Probst, Alain C Vaucher, Vishnu H Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond. 2021 · 2021
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Molclr: Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. 2021 · 2021
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Dual-view molecule pre-training
Jinhua Zhu, Yingce Xia, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2021 · 2021
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Fragnet, a contrastive learning-based transformer model for clustering, interpreting, visualizing, and navigating chemical space
Aditya Divyakant Shrivastava and Douglas B Kell. 2021 · 2065
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