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Molecular representation learning is fundamental for many drug related applications.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
Harry L Morgan · 1965
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A cluster separation measure
David L Davies and Donald W Bouldin · 1979
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Partial agonists of benzodiazepine receptors for the treatment of epilepsy, sleep, and anxiety disorders
W Haefely, M Facklam, P Schoch, JR Martin, EP Bonetti, JL Moreau, F Jenck, and JG Richards · 1992
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A guide to IUPAC Nomenclature of Organic Compounds
R Panico, WH Powell, and Jean-Claude Richer · 1993
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Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry
Xiao Qing Lewell, Duncan B Judd, Stephen P Watson, and Michael M Hann · 1998
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Esol: estimating aqueous solubility directly from molecular structure
John S Delaney · 2004
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Fragment-based drug discovery
Daniel A Erlanson, Robert S McDowell, and Tom O’Brien · 2004
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On the art of compiling and using’drug-like’chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Thousands of chemical starting points for antimalarial lead identification
Francisco-Javier Gamo, Laura M Sanz, Jaume Vidal, Cristina De Cozar, Emilio Alvarez, Jose-Luis Lavandera, Dana E Vanderwall, Darren VS Green, Vinod Kumar, Samiul Hasan, et al · 2010
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Comprehensive analysis of kinase inhibitor selectivity
Mindy I Davis, Jeremy P Hunt, Sanna Herrgard, Pietro Ciceri, Lisa M Wodicka, Gabriel Pallares, Michael Hocker, Daniel K Treiber, and Patrick P Zarrinkar · 2011
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The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid
Johannes Hachmann, Roberto Olivares-Amaya, Sule Atahan-Evrenk, Carlos Amador-Bedolla, Roel S Sánchez-Carrera, Aryeh Gold-Parker, Leslie Vogt, Anna M Brockway, and Alán Aspuru-Guzik · 2011
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Organic chemistry
Jonathan Clayden, Nick Greeves, and Stuart Warren · 2012
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Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
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Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis
Jing Tang, Agnieszka Szwajda, Sushil Shakyawar, Tao Xu, Petteri Hintsanen, Krister Wennerberg, and Tero Aittokallio · 2014
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Molecular mechanisms of aldehyde toxicity: a chemical perspective
Richard M LoPachin and Terrence Gavin · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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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
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Break down in order to build up: decomposing small molecules for fragment-based drug design with e molfrag
Tairan Liu, Misagh Naderi, Chris Alvin, Supratik Mukhopadhyay, and Michal Brylinski · 2017
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Deepdta: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Deep learning improves prediction of drug–drug and drug–food interactions
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee · 2018
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Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Pubchem 2019 update: improved access to chemical data
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
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Muffin: multi-scale feature fusion for drug–drug interaction prediction
Yujie Chen, Tengfei Ma, Xixi Yang, Jianmin Wang, Bosheng Song, and Xiangxiang Zeng · 2021
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Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
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Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast
Yuyang Wang, Rishikesh Magar, Chen Liang, and Amir Barati Farimani · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2022
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Improving molecular pretraining with complementary featurizations
Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, and Shu Wu · 2022
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Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D Manning · 2019
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Molecular sets (moses): a benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
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Chemberta: large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2020
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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
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun · 2020
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2020
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Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 2020
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Molecular contrastive learning with chemical element knowledge graph
Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2022
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Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yuxiao Dong, Chunjie Wang, Jie Tang, et al · 2022
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3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2022
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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 · 2022
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Smt-dta: Improving drug-target affinity prediction with semi-supervised multi-task training
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xia, Tao Qin, Haiguang Liu, and Tie-Yan Liu · 2022
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Pangu drug model: Learn a molecule like a human
Xinyuan Lin, Chi Xu, Zhaoping Xiong, Xinfeng Zhang, Ningxi Ni, Bolin Ni, Jianlong Chang, Ruiqing Pan, Zidong Wang, Fan Yu, et al · 2022
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Geomgcl: Geometric graph contrastive learning for molecular property prediction
Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, and Hui Xiong · 2022
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Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
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One transformer can understand both 2d & 3d molecular data
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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Energy-motivated equivariant pretraining for 3d molecular graphs
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2022
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2022
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A systematic survey of chemical pre-trained models
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z Li · 2022
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Multilingual molecular representation learning via contrastive pre-training
Zhihui Guo, Pramod Sharma, Andy Martinez, Liang Du, and Robin Abraham · 2022
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Contrastive learning of image- and structure-based representations in drug discovery
Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, and Günter Klambauer · 2022
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Mole-BERT: Rethinking pre-training graph neural networks for molecules
Anonymous · 2023
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Automated 3d pre-training for molecular property prediction
Xu Wang, Huan Zhao, Wei-wei Tu, and Quanming Yao · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
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Protein-ligand binding representation learning from fine-grained interactions
Shikun Feng, Minghao Li, Yinjun Jia, Weiying Ma, and Yanyan Lan · 2023
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Rethinking tokenizer and decoder in masked graph modeling for molecules
Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang, Kenji Kawaguchi, Xiang Wang, and Tat-Seng Chua · 2024
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May the force be with you: Unified force-centric pre-training for 3d molecular conformations
Rui Feng, Qi Zhu, Huan Tran, Binghong Chen, Aubrey Toland, Rampi Ramprasad, and Chao Zhang · 2024
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Contextual molecule representation learning from chemical reaction knowledge
Han Tang, Shikun Feng, Bicheng Lin, Yuyan Ni, JIngjing Liu, Wei-Ying Ma, and Yanyan Lan · 2024
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Drugclip: Contrasive protein-molecule representation learning for virtual screening
Bowen Gao, Bo Qiang, Haichuan Tan, Yinjun Jia, Minsi Ren, Minsi Lu, Jingjing Liu, Wei-Ying Ma, and Yanyan Lan · 2024
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