Fetching the paper…
Reading the bibliography…
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space.
S. H. Bertz, “The first general index of molecular complexity,” Journal of the American Chemical Society 103
1981
Earlier work this paper cites.
A. K. Ghose and G. M. Crippen, “Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships i. partition coefficients as a measure of hydrophobicity,” Journal of computational chemistry 7
1986
Earlier work this paper cites.
D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” Journal of chemical information and computer sciences 28
1988
Earlier work this paper cites.
R. S. Bohacek, C. McMartin, and W. C. Guida, “The art and practice of structure-based drug design: a molecular modeling perspective,” Medicinal research reviews 16
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation 9
1997
Earlier work this paper cites.
S. A. Wildman and G. M. Crippen, “Prediction of physicochemical parameters by atomic contributions,” Journal of chemical information and computer sciences 39
1999
Earlier work this paper cites.
J. J. Irwin and B. K. Shoichet, “Zinc- a free database of commercially available compounds for virtual screening,” Journal of chemical information and modeling 45
2005
Earlier work this paper cites.
P. Ertl, S. Roggo, and A. Schuffenhauer, “Natural product-likeness score and its application for prioritization of compound libraries,” Journal of chemical information and modeling 48
2008
Earlier work this paper cites.
P. Ertl and A. Schuffenhauer, “Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions,” Journal of cheminformatics 1
2009
Earlier work this paper cites.
L. C. Blum and J.-L. Reymond, “970 million druglike small molecules for virtual screening in the chemical universe database gdb-13,” Journal of the American Chemical Society 131
2009
Earlier work this paper cites.
I. Sutskever, J. Martens, and G. E. Hinton, “Generating text with recurrent neural networks,” International Conference on Machine Learning (2011) ,
2011
Earlier work this paper cites.
J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R. S. Sánchez-Carrera, A. Gold-Parker, L. Vogt, A. M. Brockway, and A. Aspuru-Guzik, “The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid,” The Journal of Physical Chemistry Letters 2
2011
Earlier work this paper cites.
G. R. Bickerton, G. V. Paolini, J. Besnard, S. Muresan, and A. L. Hopkins, “Quantifying the chemical beauty of drugs,” Nature chemistry 4
2012
Earlier work this paper cites.
S. Baldwin, in Journal of Physics: Conference Series , Vol. 341 (IOP Publishing, 2012) p. 012001
2012
Earlier work this paper cites.
2013
Cited alongside, same era.
G. Landrum, “Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling,” (2013)
2013
Cited alongside, same era.
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, in Neural Information Processing Systems (2015)
2015
Cited alongside, same era.
S. Kim, P. A. Thiessen, E. E. Bolton, J. Chen, G. Fu, A. Gindulyte, L. Han, J. He, S. He, B. A. Shoemaker, et al. , “Pubchem substance and compound databases,” Nucleic acids research 44
2016
Cited alongside, same era.
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato, in International Conference on Machine Learning 2017
2017
2018
Later among the works it cites.
2018
Later among the works it cites.
N. O’Boyle and A. Dalke, “Deepsmiles: an adaptation of smiles for use in machine-learning of chemical structures,” (2018)
2018
Later among the works it cites.
B. Samanta, D. Abir, G. Jana, P. K. Chattaraj, N. Ganguly, and M. G. Rodriguez, “Nevae: A deep generative model for molecular graphs,” AAAI Conference on Artificial Intelligence (2019) ,
2019
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.
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, “Automatic chemical design using a data-driven continuous representation of molecules,” ACS central science 4
2018
Cited alongside, same era.
M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller, “Generating focused molecule libraries for drug discovery with recurrent neural networks,” ACS central science 4
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Liu, M. Allamanis, M. Brockschmidt, and A. Gaunt, in Advances in Neural Information Processing Systems (2018) pp. 7795–7804
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. You, B. Liu, Z. Ying, V. Pande, and J. Leskovec, “Graph convolutional policy network for goal-directed molecular graph generation,” Advances in Neural Information Processing Systems , , 6410 (2018)
2018
Cited alongside, same era.
M. Simonovsky and N. Komodakis, in International Conference on Artificial Neural Networks (Springer, 2018) pp. 412–422
2018
Cited alongside, same era.
2019
Later among the works it cites.
P. C. St. John, C. Phillips, T. W. Kemper, A. N. Wilson, Y. Guan, M. F. Crowley, M. R. Nimlos, and R. E. Larsen, “Message-passing neural networks for high-throughput polymer screening,” The Journal of chemical physics 150
2019
Later among the works it cites.
2020
Later among the works it cites.
D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev, R. Kurbanov, A. Artamonov, V. Aladinskiy, M. Veselov, et al. , “Molecular sets (moses): a benchmarking platform for molecular generation models,” Frontiers in pharmacology 11
2020
Later among the works it cites.
M. Moret, L. Friedrich, F. Grisoni, D. Merk, and G. Schneider, “Generative molecular design in low data regimes,” Nature Machine Intelligence 2
2020
Later among the works it cites.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al. , “Highly accurate protein structure prediction with alphafold,” Nature 596
2021
Closest in time.
Q. Perron, O. Mirguet, H. Tajmouati, A. Skiredj, A. Rojas, A. Gohier, P. Ducrot, M.-P. Bourguignon, P. Sansilvestri-Morel, N. Do Huu, et al. , “Deep generative models for ligand-based de novo design applied to multi-parametric optimization,” (2021)
2021
Closest in time.
D. Flam-Shepherd, T. C. Wu, P. Friederich, and A. Aspuru-Guzik, “Neural message passing on high order paths,” Machine Learning: Science and Technology (2021)
2021
Closest in time.
M. A. Skinnider, R. G. Stacey, D. S. Wishart, and L. J. Foster, “Deep generative models enable navigation in sparsely populated chemical space,” (2021)
2021
Closest in time.