Fetching the paper…
Reading the bibliography…
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties.
Crafting papers on machine learning
Langley, P · 2000
Earlier work this paper cites.
A field guide to dynamical recurrent networks
Kolen, J. F. and Kremer, S. C · 2001
Earlier work this paper cites.
Conformation mining: an algorithm for finding biologically relevant conformations
Putta, S., Landrum, G. A., and Penzotti, J. E · 2005
Earlier work this paper cites.
Feature-map vectors: a new class of informative descriptors for computational drug discovery
Landrum, G. A., Penzotti, J. E., and Putta, S · 2006
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. and Schuffenhauer, A · 2009
Earlier work this paper cites.
New substructure filters for removal of pan assay interference compounds (pains) from screening libraries and for their exclusion in bioassays
Baell, J. B. and Holloway, G. A · 2010
Earlier work this paper cites.
Computationally efficient algorithm to identify matched molecular pairs (mmps) in large data sets
Hussain, J. and Rea, C · 2010
Earlier work this paper cites.
Impact of linker length on the activity of protacs
Cyrus, K., Wehenkel, M., Choi, E.-Y., Han, H.-J., Lee, H., Swanson, H., and Kim, K.-B · 2011
Earlier work this paper cites.
Compound design by fragment-linking
Ichihara, O., Barker, J., Law, R. J., and Whittaker, M · 2011
Earlier work this paper cites.
Open babel: An open chemical toolbox
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and Hutchison, G. R · 2011
Earlier work this paper cites.
Symmetry-aware actor-critic for 3d molecular design
Simm, G. N., Pinsler, R., Csányi, G., and Hernández-Lobato, J. M · 2011
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Estimation of the size of drug-like chemical space based on gdb-17 data
Polishchuk, P. G., Madzhidov, T. I., and Varnek, A · 2013
Earlier work this paper cites.
Fragment-Based Methods in Drug Discovery
Klon, A. E · 2015
Earlier work this paper cites.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
Niepert, M., Ahmed, M., and Kutzkov, K · 2016
Cited alongside, same era.
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
Cited alongside, same era.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Felix, H. E. S., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Graphnvp: An invertible flow model for generating molecular graphs
Madhawa, K., Ishiguro, K., Nakago, K., and Abe, M · 2019
Later among the works it cites.
Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2019
Later among the works it cites.
A generative model for molecular distance geometry
Simm, G. N. and Hernández-Lobato, J. M · 2019
Later among the works it cites.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
Later among the works it cites.
Deep generative models for 3d linker design
Imrie, F., Bradley, A. R., van der Schaar, M., and Deane, C. M · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dai, H., Tian, Y., Dai, B., Skiena, S., and Song, L · 2018
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
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., and Aspuru-Guzik, A · 2018
Cited alongside, same era.
Introduction to quantum mechanics
Griffiths, D. J. and Schroeter, D. F · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Cited alongside, same era.
Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 2018
Cited alongside, same era.
Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A · 2018
Cited alongside, same era.
Constrained generation of semantically valid graphs via regularizing variational autoencoders
Ma, T., Chen, J., and Xiao, C · 2018
Cited alongside, same era.
Hierarchical generation of molecular graphs using structural motifs
Jin, W., Barzilay, R., and Jaakkola, T · 2020
Later among the works it cites.
Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
Later among the works it cites.
Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2020
Later among the works it cites.
Syntalinker: automatic fragment linking with deep conditional transformer neural networks
Yang, Y., Zheng, S., Su, S., Zhao, C., Xu, J., and Chen, H · 2020
Later among the works it cites.
A tutorial on vaes: From bayes’ rule to lossless compression
Yu, R · 2020
Later among the works it cites.
Vector neurons: A general framework for so (3)-equivariant networks
Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., and Guibas, L · 2021
Later among the works it cites.
Deep generative design with 3d pharmacophoric constraints
Imrie, F., Hadfield, T. E., Bradley, A. R., and Deane, C. M · 2021
Later among the works it cites.
Spherical message passing for 3d graph networks
Liu, Y., Wang, L., Liu, M., Zhang, X., Oztekin, B., and Ji, S · 2021
Later among the works it cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K. T., Unke, O. T., and Gastegger, M · 2021
Later among the works it cites.
An autoregressive flow model for 3d molecular geometry generation from scratch
Anonymous · 2022
Closest in time.
Rdkit: Open-source cheminformatics
Landrum, G · 2022
Closest in time.