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This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables.
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Nikolaus Hansen · 2006
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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MCMC using hamiltonian dynamics
Radford M Neal · 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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Adam: A method for stochastic optimization
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Auto-encoding variational bayes
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U-net: Convolutional networks for biomedical image segmentation
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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End-to-end optimized image compression
Johannes Ballé, Valero Laparra, and Eero P Simoncelli · 2016
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An improved model for fragment-based lead generation at astrazeneca
Nathan Fuller, Loredana Spadola, Scott Cowen, Joe Patel, Heike Schönherr, Qing Cao, Andrew McKenzie, Fredrik Edfeldt, Al Rabow, and Robert Goodnow · 2016
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Serine synthesis helps hypoxic cancer stem cells regulate redox
Debangshu Samanta and Gregg L Semenza · 2016
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A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Ying Nian Wu · 2016
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Alternating back-propagation for generator network
Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Structural insights into the enzymatic activity and potential substrate promiscuity of human 3-phosphoglycerate dehydrogenase (phgdh)
Judith E Unterlass, Robert J Wood, Arnaud Baslé, Julie Tucker, Céline Cano, Martin ME Noble, and Nicola J Curtin · 2017
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The reparameterization trick for acquisition functions
James T Wilson, Riccardo Moriconi, Frank Hutter, and Marc Peter Deisenroth · 2017
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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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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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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Conditional probability models for deep image compression
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool · 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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Model-based reinforcement learning for biological sequence design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, and Lucy Colwell · 2019
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Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Molecular hypergraph grammar with its application to molecular optimization
Hiroshi Kajino · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Differentiable scaffolding tree for molecular optimization
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W Coley, and Jimeng Sun · 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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Learning to extend molecular scaffolds with structural motifs
Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt · 2021
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Accelerating AutoDock4 with GPUs and gradient-based local search
Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas F Tillack, Michel F Sanner, Andreas Koch, and Stefano Forli · 2021
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Inhibition of 3-phosphoglycerate dehydrogenase (phgdh) by indole amides abrogates de novo serine synthesis in cancer cells
Edouard Mullarky, Jiayi Xu, Anita D Robin, David J Huggins, Andy Jennings, Naoyoshi Noguchi, Andrea Olland, Damodharan Lakshminarasimhan, Michael Miller, Daisuke Tomita, et al · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
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Efficient multi-objective molecular optimization in a continuous latent space
Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert · 2019
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Learning dynamic generator model by alternating back-propagation through time
Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu · 2019
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D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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Phosphoglycerate dehydrogenase (phgdh) inhibitors: A comprehensive review 2015–2020
Quentin Spillier and Raphaël Frédérick · 2021
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Conservative objective models for effective offline model-based optimization
Brandon Trabucco, Aviral Kumar, Xinyang Geng, and Sergey Levine · 2021
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A fresh look at de novo molecular design benchmarks
Austin Tripp, Gregor NC Simm, and José Miguel Hernández-Lobato · 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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Molgensurvey: A systematic survey in machine learning models for molecule design
Yuanqi Du, Tianfan Fu, Jimeng Sun, and Shengchao Liu · 2022
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Limo: Latent inceptionism for targeted molecule generation
Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K Gilson, and Rose Yu · 2022
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Sample efficiency matters: a benchmark for practical molecular optimization
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Local latent space bayesian optimization over structured inputs
Natalie Maus, Haydn Jones, Juston Moore, Matt J Kusner, John Bradshaw, and Jacob Gardner · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Design-bench: Benchmarks for data-driven offline model-based optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, and Sergey Levine · 2022
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Group selfies: a robust fragment-based molecular string representation
Austin H Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, and Alán Aspuru-Guzik · 2023
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Multi-objective gflownets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcıa, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
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Molecule design by latent space energy-based modeling and gradual distribution shifting
Deqian Kong, Bo Pang, Tian Han, and Ying Nian Wu · 2023
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A tale of two latent flows: Learning latent space normalizing flow with short-run langevin flow for approximate inference
Jianwen Xie, Yaxuan Zhu, Yifei Xu, Dingcheng Li, and Ping Li · 2023
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Latent plan transformer for trajectory abstraction: Planning as latent space inference
Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, and Ying Nian Wu · 2024
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Sample-efficient multi-objective molecular optimization with gflownets
Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Tingjun Hou, Jian Wu, et al · 2024
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