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Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently.
Self-organization in a perceptual network
Ralph Linsker · 1988
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Principles of crystal nucleation and growth
James J De Yoreo and Peter G Vekilov · 2003
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Elements of Information Theory
Joy A. Thomas and Thomas M. Cover · 2006
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Deducing chemical structure from crystallographically determined atomic coordinates
Ian J Bruno, Gregory P Shields, and Robin Taylor · 2011
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Tweedie’s formula and selection bias
Bradley Efron · 2011
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
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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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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Continuous-time flows for efficient inference and density estimation
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, and Lawrence Carin Duke · 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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Cormorant: Covariant molecular neural networks
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Strategies for pre-training graph neural networks
W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, and J Leskovec · 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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E (n) equivariant normalizing flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, and Max Welling · 2021
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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 · 2021
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E (n) equivariant graph neural networks
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
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Equivariant energy-guided sde for inverse molecular design
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Equivariant diffusion for molecule generation in 3d
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Graphmae: Self-supervised masked graph autoencoders
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Diffusion based representation learning
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Sliced denoising: A physics-informed molecular pre-training method
Yuyan Ni, Shikun Feng, Wei-Ying Ma, Zhi-Ming Ma, and Yanyan Lan · 2023
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Non-denoising forward-time diffusions
Stefano Peluchetti · 2023
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A workflow for deriving chemical entities from crystallographic data and its application to the crystallography open database
Antanas Vaitkus, Andrius Merkys, Thomas Sander, Miguel Quirós, Paul A Thiessen, Evan E Bolton, and Saulius Gražulis · 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
Clement Vignac, Nagham Osman, Laura Toni, and Pascal Frossard · 2023
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Geomgcl: Geometric graph contrastive learning for molecular property prediction
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Understanding diffusion models: A unified perspective
Calvin Luo · 2022
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An autoregressive flow model for 3d molecular geometry generation from scratch
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3d infomax improves gnns for molecular property prediction
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and Qiang Liu · 2022
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Pre-training via denoising for molecular property prediction
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Geometric latent diffusion models for 3d molecule generation
Minkai Xu, Alexander S Powers, Ron O Dror, Stefano Ermon, and Jure Leskovec · 2023
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Latent 3d graph diffusion
Yuning You, Ruida Zhou, Jiwoong Park, Haotian Xu, Chao Tian, Zhangyang Wang, and Yang Shen · 2023
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Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
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