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How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery.
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the chembl database
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Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Dávid Bajusz, Anita Rácz, and Károly Héberger · 2015
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Mol2vec: unsupervised machine learning approach with chemical intuition
Sabrina Jaeger, Simone Fulle, and Samo Turk · 2018
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Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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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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Does william shakespeare really write hamlet? knowledge representation learning with confidence
Ruobing Xie, Zhiyuan Liu, Fen Lin, and Leyu Lin · 2018
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Development and application of a data-driven reaction classification model: comparison of an electronic lab notebook and medicinal chemistry literature
Gian Marco Ghiandoni, Michael J Bodkin, Beining Chen, Dimitar Hristozov, James EA Wallace, James Webster, and Valerie J Gillet · 2019
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Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi · 2019
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Gtranse: generalizing translation-based model on uncertain knowledge graph embedding
Natthawut Kertkeidkachorn, Xin Liu, and Ryutaro Ichise · 2019
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Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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An autoregressive flow model for 3d molecular geometry generation from scratch
Youzhi Luo and Shuiwang Ji · 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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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Mg-bert: leveraging unsupervised atomic representation learning for molecular property prediction
Xiao-Chen Zhang, Cheng-Kun Wu, Zhi-Jiang Yang, Zhen-Xing Wu, Jia-Cai Yi, Chang-Yu Hsieh, Ting-Jun Hou, and Dong-Sheng Cao · 2021
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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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed · 2020
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Graph representation learning
William L Hamilton · 2020
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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 · 2020
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Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, and Sen Song · 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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Imdrug: A benchmark for deep imbalanced learning in ai-aided drug discovery
Lanqing Li, Liang Zeng, Ziqi Gao, Shen Yuan, Yatao Bian, Bingzhe Wu, Hengtong Zhang, Chan Lu, Yang Yu, Wei Liu, et al · 2022
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An adaptive graph learning method for automated molecular interactions and properties predictions
Yuquan Li, Chang-Yu Hsieh, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, et al · 2022
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Structured multi-task learning for molecular property prediction
Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, and Jian Tang · 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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Unified deep learning model for multitask reaction predictions with explanation
Jieyu Lu and Yingkai Zhang · 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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Chemical-reaction-aware molecule representation learning
Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, and Martin D Burke · 2022
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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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Simgrace: A simple framework for graph contrastive learning without data augmentation
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Mole-BERT: Rethinking pre-training graph neural networks for molecules
Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z. Li · 2023
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