Fetching the paper…
Reading the bibliography…
In this work we introduce an Autoencoder for molecular conformations.
Kennedy, J.; Eberhart, R. Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks. 1995; pp 1942–1948
1948
Earlier work this paper cites.
Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of computational chemistry 1996
1996
Earlier work this paper cites.
Landrum, G., et al. RDKit: Open-source cheminformatics. 2006
2006
Earlier work this paper cites.
Todeschini, R.; Consonni, V. Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references ; John Wiley & Sons, 2009; Vol. 41
2009
Earlier work this paper cites.
Hawkins, P. C.; Skillman, A. G.; Warren, G. L.; Ellingson, B. A.; Stahl, M. T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. Journal of chemical information and modeling 2010
2010
Earlier work this paper cites.
Bolton, E. E.; Chen, J.; Kim, S.; Han, L.; He, S.; Shi, W.; Simonyan, V.; Sun, Y.; Thiessen, P. A.; Wang, J., et al. PubChem3D: a new resource for scientists. Journal of cheminformatics 2011
2011
Earlier work this paper cites.
Bickerton, G. R.; Paolini, G. V.; Besnard, J.; Muresan, S.; Hopkins, A. L. Quantifying the chemical beauty of drugs. Nature chemistry 2012
2012
Earlier work this paper cites.
Kingma, D. P.; Welling, M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 2013
2013
Earlier work this paper cites.
Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems. 2015; pp 2224–2232
2015
Cited alongside, same era.
Riniker, S.; Landrum, G. A. Better informed distance geometry: using what we know to improve conformation generation. Journal of chemical information and modeling 2015
2015
Cited alongside, same era.
2017
Cited alongside, same era.
Simonovsky, M.; Komodakis, N. Dynamic edge-conditioned filters in convolutional neural networks on graphs. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; pp 3693–3702
2017
Cited alongside, same era.
Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R. SchNet–A deep learning architecture for molecules and materials. The Journal of Chemical Physics 2018
2018
Later among the works it cites.
Winter, R.; Montanari, F.; Steffen, A.; Briem, H.; Noé, F.; Clevert, D.-A. Efficient multi-objective molecular optimization in a continuous latent space. Chemical science 2019
2019
Later among the works it cites.
Mansimov, E.; Mahmood, O.; Kang, S.; Cho, K. Molecular geometry prediction using a deep generative graph neural network. Scientific reports 2019
2019
Later among the works it cites.
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…
Zaheer, M.; Kottur, S.; Ravanbakhsh, S.; Poczos, B.; Salakhutdinov, R. R.; Smola, A. J. Deep sets. Advances in neural information processing systems. 2017; pp 3391–3401
2017
Cited alongside, same era.
Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science 2018
2018
Cited alongside, same era.
Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. 2018
2018
Cited alongside, same era.
Segler, M. H.; Kogej, T.; Tyrchan, C.; Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
Winter, R.; Montanari, F.; Noé, F.; Clevert, D.-A. Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chemical science 2019
2019
Later among the works it cites.
Montanari, F.; Kuhnke, L.; Ter Laak, A.; Clevert, D.-A. Modeling physico-chemical admet endpoints with multitask graph convolutional networks. Molecules 2020
2020
Later among the works it cites.
Le, T.; Winter, R.; Noé, F.; Clevert, D.-A. Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures. Chem. Sci. 2020
2020
Later among the works it cites.