Fetching the paper…
Reading the bibliography…
A fundamental problem in drug discovery is to design molecules that bind to specific proteins.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
Earlier work this paper cites.
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K., Goddard III, W. A., and Skiff, W. M · 1992
Earlier work this paper cites.
The process of structure-based drug design
Anderson, A. C · 2003
Earlier work this paper cites.
RDKit: Open-source cheminformatics
Landrum, G. et al · 2006
Earlier work this paper cites.
AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. and Olson, A. J · 2010
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.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
Earlier work this paper cites.
3d convolutional neural networks for human action recognition
Ji, S., Xu, W., Yang, M., and Yu, K · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Earlier work this paper cites.
Grammar variational autoencoder
Kusner, M., Paige, B., and Hernández-Lobato, J · 2017
Earlier work this paper cites.
Forging the basis for developing protein–ligand interaction scoring functions
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., and Wang, R · 2017
Earlier work this paper cites.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Earlier work this paper cites.
Protein–ligand scoring with convolutional neural networks
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., and Koes, D. R · 2017
Earlier work this paper cites.
Syntax-directed variational autoencoder for structured data
Dai, H., Tian, Y., Dai, B., Skiena, S., and Song, L · 2018
Cited alongside, same era.
MolGAN: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, T · 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.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Cited alongside, same era.
TorsionNet: A reinforcement learning approach to sequential conformer search
Gogineni, T., Xu, Z., Punzalan, E., Jiang, R., Kammeraad, J., Tewari, A., and Zimmerman, P · 2020
Later among the works it cites.
3DMolNet: a generative network for molecular structures
Nesterov, V., Wieser, M., and Roth, V · 2020
Later among the works it cites.
A generative model for molecular distance geometry
Simm, G. and Hernandez-Lobato, J. M · 2020
Later among the works it cites.
Reinforcement learning for molecular design guided by quantum mechanics
Simm, G., Pinsler, R., and Hernández-Lobato, J. M · 2020
Later among the works it cites.
MoFlow: an invertible flow model for generating molecular graphs
Zang, C. and Wang, F · 2020
Later among the works it cites.
GeoMol: Torsional geometric generation of molecular 3d conformer ensembles
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
Cited alongside, same era.
GraphVAE: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N · 2018
Cited alongside, same era.
Flow-based deep generative models
Weng, L · 2018
Cited alongside, same era.
Graphrnn: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W., and Leskovec, J · 2018
Cited alongside, same era.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N. W., Gastegger, M., and Schütt, K. T · 2019
Cited alongside, same era.
Generating valid euclidean distance matrices
Hoffmann, M. and Noé, F · 2019
Cited alongside, same era.
Ganea, O.-E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., and Jaakkola, T. S · 2021
Later among the works it cites.
Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
Later among the works it cites.
Gemnet: Universal directional graph neural networks for molecules
Klicpera, J., Becker, F., and Günnemann, S · 2021
Later among the works it cites.
Gnina 1.0: molecular docking with deep learning
McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., and Koes, D. R · 2021
Later among the works it cites.
Generating 3d molecules conditional on receptor binding sites with deep generative models
Ragoza, M., Masuda, T., and Koes, D. R · 2021
Later among the works it cites.
E(n) equivariant normalizing flows for molecule generation in 3d
Satorras, V. G., Hoogeboom, E., Fuchs, F. B., Posner, I., and Welling, M · 2021
Later among the works it cites.
Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
Later among the works it cites.
An end-to-end framework for molecular conformation generation via bilevel programming
Xu, M., Wang, W., Luo, S., Shi, C., Bengio, Y., Gomez-Bombarelli, R., and Tang, J · 2021
Later among the works it cites.
MolGenSurvey: A systematic survey in machine learning models for molecule design
Du, Y., Fu, T., Sun, J., and Liu, S · 2022
Closest in time.
Spherical message passing for 3d molecular graphs
Liu, Y., Wang, L., Liu, M., Lin, Y., Zhang, X., Oztekin, B., and Ji, S · 2022
Closest in time.
An autoregressive flow model for 3d molecular geometry generation from scratch
Luo, Y. and Ji, S · 2022
Closest in time.