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Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery.
Symmetry in chemical structures and reactions
Alexandru T Balaban · 1986
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Symmetry arguments in chemistry
Jack D Dunitz · 1996
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The upper bound revisited
Lloyd M Robeson · 2008
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Rules for identifying potentially reactive or promiscuous compounds
Robert F Bruns and Ian A Watson · 2012
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Polymer gas separation membrane database, 2012
A Thornton, L Robeson, B Freeman, and D Uhlmann · 2012
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 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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Scscore: synthetic complexity learned from a reaction corpus
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2018
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 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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Polyimides for gas separation
Michael Langsam · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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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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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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On failure modes in molecule generation and optimization
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter, and Günter Klambauer · 2019
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Designing exceptional gas-separation polymer membranes using machine learning
J Wesley Barnett, Connor R Bilchak, Yiwen Wang, Brian C Benicewicz, Laura A Murdock, Tristan Bereau, and Sanat K Kumar · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Pi1m: a benchmark database for polymer informatics
Generative models for molecular discovery: Recent advances and challenges
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F Jensen · 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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Inverse design of 3d molecular structures with conditional generative neural networks
Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert Müller, and Kristof T Schütt · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Ruimin Ma and Tengfei Luo · 2020
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Goal-directed generation of discrete structures with conditional generative models
Amina Mollaysa, Brooks Paige, and Alexandros Kalousis · 2020
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Permutation invariant graph generation via score-based generative modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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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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Score-based generative modeling of graphs via the system of stochastic differential equations
Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
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Graph rationalization with environment-based augmentations
Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang · 2022
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Equivariant energy-guided SDE for inverse molecular design
Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, and Jun Zhu · 2023
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Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Genetic algorithms are strong baselines for molecule generation
Austin Tripp and José Miguel Hernández-Lobato · 2023
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Guided diffusion for inverse molecular design
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M Bronstein, and Renana Gershoni-Poranne · 2023
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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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Sample-efficient multi-objective molecular optimization with gflownets
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