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Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
A decade of fragment-based drug design: strategic advances and lessons learned
Philip J Hajduk and Jonathan Greer · 2007
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Visualizing data using t-sne
Van Der Maaten Laurens and Geoffrey Hinton · 2008
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Design, synthesis and selection of dna-encoded small-molecule libraries
Matthew A Clark, Raksha A Acharya, Christopher C Arico-Muendel, Svetlana L Belyanskaya, Dennis R Benjamin, Neil R Carlson, Paolo A Centrella, Cynthia H Chiu, Steffen P Creaser, John W Cuozzo, et al · 2009
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Pubchem: a public information system for analyzing bioactivities of small molecules
Yanli Wang, Jewen Xiao, Tugba O Suzek, Jian Zhang, Jiyao Wang, and Stephen H Bryant · 2009
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Effectiveness of 2d fingerprints for scaffold hopping
Eleanor J Gardiner, Caroline Holliday, John D andǾD owd, and Peter Willett · 2011
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Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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A bayesian approach to in silico blood-brain barrier penetration modeling
Ines Filipa Martins, Ana L Teixeira, Luis Pinheiro, and Andre O Falcao · 2012
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Sweetlead: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery
Paul A Novick, Oscar F Ortiz, Jared Poelman, Amir Y Abdulhay, and Vijay S Pande · 2013
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URL https://tripod.nih.gov/tox21/challenge/
Tox21 data challenge 2014 · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Counting on natural products for drug design
Tiago Rodrigues, Daniel Reker, Petra Schneider, and Gisbert Schneider · 2016
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Toxcast chemical landscape: paving the road to 21st century toxicology
Ann M Richard, Richard S Judson, Keith A Houck, Christopher M Grulke, Patra Volarath, Inthirany Thillainadarajah, Chihae Yang, James Rathman, Matthew T Martin, John F Wambaugh, et al · 2016
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The sider database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 2016
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Computational modeling of
Govindan Subramanian, Bharath Ramsundar, Vijay Pande, and Rajiah Aldrin Denny · 2016
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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URL https://www.ctti-clinicaltrials.org/aact-database
Aact database · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Shuaihua Lu, Qionghua Zhou, Yixin Ouyang, Yilv Guo, Qiang Li, and Jinlan Wang · 2018
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 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
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Predicting molecular properties with covariant compositional networks
Truong Son Hy, Shubhendu Trivedi, Horace Pan, Brandon M Anderson, and Risi Kondor · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 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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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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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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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 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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Masked graph modeling for molecule generation
Omar Mahmood, Elman Mansimov, Richard Bonneau, and Kyunghyun Cho · 2021
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Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, et al · 2021
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Chemical-reaction-aware molecule representation learning
Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, and Martin Burke · 2021
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Biomedical event extraction based on knowledge-driven tree-lstm
Diya Li, Lifu Huang, Heng Ji, and Jiawei Han · 2019
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Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
Cited alongside, same era.
Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen · 2019
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Algebraic graph-assisted bidirectional transformers for molecular property prediction
Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, and Feng Pan · 2021
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Gf-vae: A flow-based variational autoencoder for molecule generation
Changsheng Ma and Xiangliang Zhang · 2021
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Graphdf: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
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A deep generative model for molecule optimization via one fragment modification
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, and Xia Ning · 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
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan · 2021
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Text2mol: Cross-modal molecule retrieval with natural language queries
Carl Edwards, ChengXiang Zhai, and Heng Ji · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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Improving de novo molecular design with curriculum learning
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, and Atanas Patronov · 2022
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Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2022
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Optimizing molecules using efficient queries from property evaluations
Samuel C Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das · 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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Glam: 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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Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
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Mgcvae: Multi-objective inverse design via molecular graph conditional variational autoencoder
Myeonghun Lee and Kyoungmin Min · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zheni Zeng, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2022
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Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, et al · 2022
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Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, and Heng Ji · 2022
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Bootstrapping informative graph augmentation via a meta learning approach
Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Changwen Zheng, and Fuchun Sun · 2022
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