Fetching the paper…
Reading the bibliography…
Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules.
Handbook of stochastic methods , volume 3
C. W. Gardiner et al · 1985
Earlier work this paper cites.
Diffusion for global optimization in r
T.-S. Chiang, C.-R. Hwang, and S. J. Sheu · 1987
Earlier work this paper cites.
Recursive stochastic algorithms for global optimization in r
S. B. Gelfand and S. K. Mitter · 1991
Earlier work this paper cites.
The art and practice of structure-based drug design: a molecular modeling perspective
R. S. Bohacek, C. McMartin, and W. C. Guida · 1996
Earlier work this paper cites.
The variational formulation of the fokker-planck equation
R. Jordan, D. Kinderlehrer, and F. Otto · 1996
Earlier work this paper cites.
A computational fluid mechanics solution to the monge-kantorovich mass transfer problem
J.-D. Benamou and Y. Brenier · 2000
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
L. Ambrosio, N. Gigli, and G. Savaré · 2005
Earlier work this paper cites.
Zinc- a free database of commercially available compounds for virtual screening
J. J. Irwin and B. K. Shoichet · 2005
Earlier work this paper cites.
Computational methods in developing quantitative structure-activity relationships (qsar): a review
A. Z. Dudek, T. Arodz, and J. Gálvez · 2006
Earlier work this paper cites.
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
Earlier work this paper cites.
Deep learning , volume 1
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
Earlier work this paper cites.
Objective-reinforced generative adversarial networks (organ) for sequence generation models
G. L. Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. L. C. Farias, and A. Aspuru-Guzik · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Earlier work this paper cites.
ChemTS: an efficient python library for de novo molecular generation
X. Yang, J. Zhang, K. Yoshizoe, K. Terayama, and K. Tsuda · 2017
Earlier work this paper cites.
Understanding disentangling in
C. P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, G. Desjardins, and A. Lerchner · 2018
Earlier work this paper cites.
MolGAN: An implicit generative model for small molecular graphs, 2018
N. D. Cao and T. Kipf · 2018
Earlier work this paper cites.
Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
Earlier work this paper cites.
Constrained graph variational autoencoders for molecule design
Q. Liu, M. Allamanis, M. Brockschmidt, and A. Gaunt · 2018
Earlier work this paper cites.
Molecular sets (moses): A benchmarking platform for molecular generation models
D. Polykovskiy, A. Zhebrak, B. Sánchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev, R. Kurbanov, A. A. Artamonov, V. Aladinskiy, M. Veselov, A. Kadurin, S. I. Nikolenko, A. Aspuru-Guzik, and A. Zhavoronkov · 2018
Earlier work this paper cites.
Inverse molecular design using machine learning: Generative models for matter engineering
B. Sanchez-Lengeling and A. Aspuru-Guzik · 2018
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning
Z. Zhou, S. M. Kearnes, L. Li, R. N. Zare, and P. F. Riley · 2018
Cited alongside, same era.
Guacamol: benchmarking models for de novo molecular design
N. Brown, M. Fiscato, M. H. Segler, and A. C. Vaucher · 2019
Cited alongside, same era.
Ganalyze: Toward visual definitions of cognitive image properties
L. Goetschalckx, A. Andonian, A. Oliva, and P. Isola · 2019
Cited alongside, same era.
On the" steerability" of generative adversarial networks
A. Jahanian, L. Chai, and P. Isola · 2019
Cited alongside, same era.
A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Mars: Markov molecular sampling for multi-objective drug discovery
Y. Xie, C. Shi, H. Zhou, Y. Yang, W. Zhang, Y. Yu, and L. Li · 2021
Later among the works it cites.
Interpretable molecular graph generation via monotonic constraints
Y. Du, X. Guo, A. Shehu, and L. Zhao · 2022
Later among the works it cites.
Limo: Latent inceptionism for targeted molecule generation
P. Eckmann, K. Sun, B. Zhao, M. Feng, M. Gilson, and R. Yu · 2022
Later among the works it cites.
Reinforced genetic algorithm for structure-based drug design
T. Fu, W. Gao, C. Coley, and J. Sun · 2022
Later among the works it cites.
Equivariant diffusion for molecule generation in 3d
E. Hoogeboom, V. G. Satorras, C. Vignac, and M. Welling · 2022
Later among the works it cites.
Artificial intelligence foundation for therapeutic science
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. H. Jensen · 2019
Cited alongside, same era.
Self-referencing embedded strings (selfies): A 100% robust molecular string representation
M. Krenn, F. Hase, A. Nigam, P. Friederich, and A. Aspuru-Guzik · 2019
Cited alongside, same era.
Graphnvp: An invertible flow model for generating molecular graphs
K. Madhawa, K. Ishiguro, K. Nakago, and M. Abe · 2019
Cited alongside, same era.
Applications of machine learning in drug discovery and development
J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, et al · 2019
Cited alongside, same era.
Constrained bayesian optimization for automatic chemical design using variational autoencoders
R.-R. Griffiths and J. M. Hernández-Lobato · 2020
Cited alongside, same era.
Ganspace: Discovering interpretable gan controls
E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik · 2022
Later among the works it cites.
Score-based generative modeling of graphs via the system of stochastic differential equations
J. Jo, S. Lee, and S. J. Hwang · 2022
Later among the works it cites.
Diffusion models already have a semantic latent space
M. Kwon, J. Jeong, and Y. Uh · 2022
Later among the works it cites.
Structure-based drug design with equivariant diffusion models
A. Schneuing, Y. Du, C. Harris, A. Jamasb, I. Igashov, W. Du, T. Blundell, P. Lió, C. Gomes, M. Welling, et al · 2022
Later among the works it cites.
Orthogonal svd covariance conditioning and latent disentanglement
Y. Song, N. Sebe, and W. Wang · 2022
Later among the works it cites.
Chemspace: Interpretable and interactive chemical space exploration
Y. Du, X. Liu, N. M. Shah, S. Liu, J. Zhang, and B. Zhou · 2023
Later among the works it cites.
Illuminating protein space with a programmable generative model
J. B. Ingraham, M. Baranov, Z. Costello, K. W. Barber, W. Wang, A. Ismail, V. Frappier, D. M. Lord, C. Ng-Thow-Hing, E. R. Van Vlack, et al · 2023
Later among the works it cites.
Reinvent4: Modern ai–driven generative molecule design
H. Loeffler, J. He, A. Tibo, J. P. Janet, A. Voronov, L. Mervin, and O. Engkvist · 2023
Later among the works it cites.
Digress: Discrete denoising diffusion for graph generation
C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard · 2023
Later among the works it cites.
Scientific discovery in the age of artificial intelligence
H. Wang, T. Fu, Y. Du, W. Gao, K. Huang, Z. Liu, P. Chandak, S. Liu, P. Van Katwyk, A. Deac, et al · 2023
Later among the works it cites.
De novo design of protein structure and function with rfdiffusion
J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al · 2023
Later among the works it cites.
The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods
B. Zdrazil, E. Felix, F. Hunter, E. J. Manners, J. Blackshaw, S. Corbett, M. de Veij, H. Ioannidis, D. M. Lopez, J. F. Mosquera, M. P. Magariños, N. Bosc, R. Arcila, T. Kizilören, A. Gaulton, A. P. Bento, M. F. Adasme, P. Monecke, G. A. Landrum, and A. R. Leach · 2023
Later among the works it cites.
Mattergen: a generative model for inorganic materials design
C. Zeni, R. Pinsler, D. Zügner, A. Fowler, M. Horton, X. Fu, S. Shysheya, J. Crabbé, L. Sun, J. Smith, et al · 2023
Later among the works it cites.
Artificial intelligence for science in quantum, atomistic, and continuum systems
X. Zhang, L. Wang, J. Helwig, Y. Luo, C. Fu, Y. Xie, M. Liu, Y. Lin, Z. Xu, K. Yan, et al · 2023
Later among the works it cites.
Unsupervised representation learning from sparse transformation analysis
Y. Song, T. A. Keller, Y. Yue, P. Perona, and M. Welling · 2024
Closest in time.
Latent 3d graph diffusion
Y. You, R. Zhou, J. Park, H. Xu, C. Tian, Z. Wang, and Y. Shen · 2024
Closest in time.