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Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
The art and practice of structure-based drug design: a molecular modeling perspective
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Accurate ab initio quantum chemical determination of the relative energetics of peptide conformations and assessment of empirical force fields
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Substructure, subgraph, and walk counts as measures of the complexity of graphs and molecules
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Furans, thiophenes and related heterocycles in drug discovery
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Biological activities of guanidine compounds
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Scaffold hopping using two-dimensional fingerprints: True potential, black magic, or a hopeless endeavor? guidelines for virtual screening
Martin Vogt, Dagmar Stumpfe, Hanna Geppert, and Jürgen Bajorath · 2010
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Halogen atoms in the modern medicinal chemistry: hints for the drug design
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Tomislav Došlic, Boris Furtula, Ante Graovac, Ivan Gutman, Sirous Moradi, and Zahra Yarahmadi · 2011
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Link prediction in complex networks: A survey
Linyuan Lü and Tao Zhou · 2011
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Frank Emmert-Streib · 2012
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Allylic oxidations in natural product synthesis
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Organic chemistry with biological applications
John E McMurry · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
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Scalable and sustainable electrochemical allylic c–h oxidation
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Thiazole: A review on chemistry, synthesis and therapeutic importance of its derivatives
Mahesh T Chhabria, Shivani Patel, Palmi Modi, and Pathik S Brahmkshatriya · 2016
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Inductive representation learning on large graphs
William Hamilton, Rex Ying, and Jure Leskovec · 2017
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 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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Catalytic allylic oxidation of internal alkenes to a multifunctional chiral building block
Liela Bayeh, Phong Q Le, and Uttam K Tambar · 2017
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang · 2018
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Moleculenet: a benchmark for molecular machine learning
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Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview
Veronica Salmaso and Stefano Moro · 2018
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A sobering assessment of small-molecule force field methods for low energy conformer predictions
Ilana Y Kanal, John A Keith, and Geoffrey R Hutchison · 2018
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Biomedical applications of aromatic azo compounds
Yousaf Ali, Shafida A Hamid, and Umer Rashid · 2018
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Role of pyridines in medicinal chemistry and design of BACE1 inhibitors possessing a pyridine scaffold
Yoshio Hamada · 2018
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Therapeutic importance of synthetic thiophene
Rashmi Shah and Prabhakar Kumar Verma · 2018
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning · 2019
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BERT rediscovers the classical NLP pipeline
Ian Tenney et al · 2019
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What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah · 2019
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, et al · 2019
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Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, et al · 2019
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, et al · 2021
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Discovery of novel chemical reactions by deep generative recurrent neural network
William Bort, Igor Baskin, Timur Gimadiev, Artem Mukanov, et al · 2021
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Graph energy-based model for substructure preserving molecular design
Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe · 2021
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On feature decorrelation in self-supervised learning
Tianyu Hua, Wenxiao Wang, Zihui Xue, Sucheng Ren, Yue Wang, et al · 2021
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Cgrtools: Python library for molecule, reaction, and condensed graph of reaction processing
Ramil I Nugmanov, Ravil N Mukhametgaleev, Tagir Akhmetshin, Timur R Gimadiev, Valentina A Afonina, et al · 2019
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A bayesian approach to predict solubility parameters
Benjamin Sanchez-Lengeling, Loïc M Roch, José Darío Perea, Stefan Langner, Christoph J Brabec, et al · 2019
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A comprehensive review on biological activities of oxazole derivatives
Saloni Kakkar and Balasubramanian Narasimhan · 2019
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Tetrazoles via multicomponent reactions
Constantinos G Neochoritis, Ting Zhao, and Alexander Domling · 2019
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Qsar without borders
Eugene N Muratov, Jürgen Bajorath, Robert P Sheridan, Igor V Tetko, Dmitry Filimonov, et al · 2020
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You can have better graph neural networks by not training weights at all: Finding untrained graph tickets
Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, et al · 2021
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Atom3d: Tasks on molecules in three dimensions
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Self-supervised learning on graphs: Contrastive, generative, or predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li · 2021
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The expanding role of pyridine and dihydropyridine scaffolds in drug design
Yong Ling, Zhi-You Hao, Dong Liang, Chun-Lei Zhang, Yan-Fei Liu, and Yan Wang · 2021
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Molecular contrastive learning of representations via graph neural networks
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Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models
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