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Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields.
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Melatonin receptor antagonists that differentiate between the human mel1a and mel1b recombinant subtypes are used to assess the pharmacological profile of the rabbit retina ml1 presynaptic heteroreceptor
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The process of structure-based drug design
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Patchdock and symmdock: servers for rigid and symmetric docking
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
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Quantifying the chemical beauty of drugs
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel · 2016
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Neural message passing for quantum chemistry
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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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Hdock: a web server for protein–protein and protein–dna/rna docking based on a hybrid strategy
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 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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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Conditional molecular design with deep generative models
Seokho Kang and Kyunghyun Cho · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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Constrained generation of semantically valid graphs via regularizing variational autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
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Adversarial threshold neural computer for molecular de novo design
Evgeny Putin, Arip Asadulaev, Quentin Vanhaelen, Yan Ivanenkov, Anastasia V Aladinskaya, Alex Aliper, and Alex Zhavoronkov · 2018
Scaffold-based molecular design with a graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, and Woo Youn Kim · 2020
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Mol-cyclegan: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł · 2020
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A machine learning approach for drug-target interaction prediction using wrapper feature selection and class balancing
Shweta Redkar, Sukanta Mondal, Alex Joseph, and KS Hareesha · 2020
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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 · 2020
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Big data and artificial intelligence modeling for drug discovery
Hao Zhu · 2020
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
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Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 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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Deep graph generators: A survey
Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah, and Hamid R Rabiee · 2021
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Mimosa: Multi-constraint molecule sampling for molecule optimization
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, and Jimeng Sun · 2021
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E(n) equivariant normalizing flows
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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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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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{MARS}: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Spanning tree-based graph generation for molecules
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Data-efficient graph grammar learning for molecular generation
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A systematic survey on deep generative models for graph generation
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Score-based generative modeling of graphs via the system of stochastic differential equations
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An autoregressive flow model for 3d molecular geometry generation from scratch
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Learning to extend molecular scaffolds with structural motifs
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Generating realistic 3d molecules with an equivariant conditional likelihood model, 2022
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Multi-resolution spectral coherence for graph generation with score-based diffusion
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De novo molecular generation via connection-aware motif mining
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