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Self-supervised pre-training is gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pre-trained models with the promise that they can extract better feature representations for molecules.
Scaffold hopping
Hans-Joachim Böhm, Alexander Flohr, and Martin Stahl · 2004
Earlier work this paper cites.
A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification
Qingliang Li, Andreas Bender, Jianfeng Pei, and Luhua Lai · 2007
Earlier work this paper cites.
Riemannian manifold learning
Tong Lin and Hongbin Zha · 2008
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Activity cliffs and activity cliff generators based on chemotype-related activity landscapes
Jaime Pérez-Villanueva, Oscar Méndez-Lucio, Olivia Soria-Arteche, and José L Medina-Franco · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Convolutional embedding of attributed molecular graphs for physical property prediction
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, and Klavs F Jensen · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
Earlier work this paper cites.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
Earlier work this paper cites.
Resimnet: drug response similarity prediction using siamese neural networks
Minji Jeon, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, Miyoung Ko, Sunkyu Kim, Yonghwa Choi, Aik-Choon Tan, and Jaewoo Kang · 2019
Cited alongside, same era.
Transferability and hardness of supervised classification tasks
Anh T Tran, Cuong V Nguyen, and Tal Hassner · 2019
Cited alongside, same era.
Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, et al · 2019
Cited alongside, same era.
Molecule attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 2020
Cited alongside, same era.
Leep: A new measure to evaluate transferability of learned representations
Cuong Nguyen, Tal Hassner, Matthias Seeger, and Cedric Archambeau · 2020
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Frustratingly easy transferability estimation
Long-Kai Huang, Ying Wei, Yu Rong, Qiang Yang, and Junzhou Huang · 2021
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A merged molecular representation learning for molecular properties prediction with a web-based service
Hyunseob Kim, Jeongcheol Lee, Sunil Ahn, and Jongsuk Ruth Lee · 2021
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Using domain-specific fingerprints generated through neural networks to enhance ligand-based virtual screening
Janosch Menke and Oliver Koch · 2021
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Mdeepred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
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Top: A deep mixture representation learning method for boosting molecular toxicity prediction
Yuzhong Peng, Ziqiao Zhang, Qizhi Jiang, Jihong Guan, and Shuigeng Zhou · 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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Rethinking drug design in the artificial intelligence era
Petra Schneider, W Patrick Walters, Alleyn T Plowright, Norman Sieroka, Jennifer Listgarten, Robert A Goodnow, Jasmin Fisher, Johanna M Jansen, José S Duca, Thomas S Rush, et al · 2020
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Communicative representation learning on attributed molecular graphs
Ying Song, Shuangjia Zheng, Zhangming Niu, Zhang-Hua Fu, Yutong Lu, and Yuedong Yang · 2020
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Extracting predictive representations from hundreds of millions of molecules
Dong Chen, Jiaxin Zheng, Guo-Wei Wei, and Feng Pan · 2021
Cited alongside, same era.
Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee
Cited in the paper.
Fragat: a fragment-oriented multi-scale graph attention model for molecular property prediction
Ziqiao Zhang, Jihong Guan, and Shuigeng Zhou
Cited in the paper.
Ahmet Süreyya Rifaioglu, Rengül Cetin Atalay, D Cansen Kahraman, Tunca Doğan, Maria Martin, and Volkan Atalay · 2021
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Self-supervised graph-level representation learning with local and global structure
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang · 2021
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Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, and Yatao Bian · 2022
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