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In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design and drug screening.
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Freesolv: a database of experimental and calculated hydration free energies, with input files
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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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Gated graph sequence neural networks
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Electronic spectra from tddft and machine learning in chemical space
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Order matters: Sequence to sequence for sets
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
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Semi-supervised classification with graph convolutional networks
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The chembl database in 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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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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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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Machine learning: new ideas and tools in environmental science and engineering
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 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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Convolutional neural networks on graphs with chebyshev approximation, revisited
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Graph networks as a universal machine learning framework for molecules and crystals
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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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Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S Zemel · 2019
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Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
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Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
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Mingguo He, Zhewei Wei, and Ji-Rong Wen · 2022
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Kpgt: knowledge-guided pre-training of graph transformer for molecular property prediction
Han Li, Dan Zhao, and Jianyang Zeng · 2022
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The expressive power of graph neural networks
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Deep reinforcement learning for inverse inorganic materials design
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A generalized framework for microstructural optimization using neural networks
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Black-box tuning for language-model-as-a-service
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Chain-of-thought prompting elicits reasoning in large language models
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael M Bronstein · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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A survey on oversmoothing in graph neural networks
T Konstantin Rusch, Michael M Bronstein, and Siddhartha Mishra · 2023
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Llama: Open and efficient foundation language models
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Artificial intelligence to advance earth observation: a perspective
Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N van Rijn, Holger H Hoos, Fabio Del Frate, et al · 2023
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How will generative ai disrupt data science in drug discovery?
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Tree of thoughts: Deliberate problem solving with large language models
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Selformer: Molecular representation learning via selfies language models
Atakan Yüksel, Erva Ulusoy, Atabey Ünlü, and Tunca Doğan · 2023
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