Fetching the paper…
Reading the bibliography…
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
The hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
Earlier work this paper cites.
The trimmed iterative closest point algorithm
Dmitry Chetverikov, Dmitry Svirko, Dmitry Stepanov, and Pavel Krsek · 2002
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Boosting docking-based virtual screening with deep learning
Janaina Cruz Pereira, Ernesto Raul Caffarena, and Cicero Nogueira Dos Santos · 2016
Earlier work this paper cites.
On implementing 2d rectangular assignment algorithms
David F. Crouse · 2016
Earlier work this paper cites.
Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Earlier work this paper cites.
Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
Earlier work this paper cites.
Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 2019
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
Earlier work this paper cites.
A purely algebraic justification of the kabsch-umeyama algorithm
Jim Lawrence, Javier Bernal, and Christoph Witzgall · 2019
Cited alongside, same era.
A review of deep learning methods for antibodies
Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S Vince Parish, Brenda Medellin, and Monica Berrondo · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
Cited alongside, same era.
Tomohide Masuda, Matthew Ragoza, and David Ryan Koes · 2020
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Later among the works it cites.
Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Diffusion-LM improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto · 2022
Later among the works it cites.
Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
Cited alongside, same era.
Formal limitations on the measurement of mutual information
David McAllester and Karl Stratos · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
Cited alongside, same era.
An autoregressive flow model for 3d molecular geometry generation from scratch
Youzhi Luo and Shuiwang Ji · 2021
Cited alongside, same era.
E (n) equivariant normalizing flows for molecule generation in 3d
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B Fuchs, Ingmar Posner, and Max Welling · 2021
Cited alongside, same era.
Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Qiang Liu · 2022
Later among the works it cites.
Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
Later among the works it cites.
Fragment-based ligand generation guided by geometric deep learning on protein-ligand structure
Alexander S. Powers, Helen H. Yu, Patricia Suriana, and Ron O. Dror · 2022
Later among the works it cites.
Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and qiang liu · 2022
Later among the works it cites.
Diffbp: Generative diffusion of 3d molecules for target protein binding
Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, and Stan Z Li · 2022
Later among the works it cites.
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
Later among the works it cites.
Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
Later among the works it cites.
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2022
Later among the works it cites.
Multisample flow matching: Straightening flows with minibatch couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky Chen · 2023
Closest in time.
Conditional flow matching: Simulation-free dynamic optimal transport
Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, and Yoshua Bengio · 2023
Closest in time.
Leon Klein, Andreas Krämer, and Frank Noé · 2023
Closest in time.
Geometric latent diffusion models for 3d molecule generation
Minkai Xu, Alexander Powers, Ron Dror, Stefano Ermon, and Jure Leskovec · 2023
Closest in time.