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The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design.
Sur la distance de deux lois de probabilité
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Markov processes over denumerable products of spaces describing large systems of automata
Wasserstein, L. N. (1969) · 1969
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The Fréchet distance between multivariate normal distributions
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
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Long short-term memory
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Prediction of physicochemical parameters by atomic contributions
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
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Extended-connectivity fingerprints
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Quantifying the chemical beauty of drugs
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ZINC: A free tool to discover chemistry for biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G. (2012) · 2012
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The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., Nowotka, M., Papadatos, G., Santos, R., and Overington, J. P. (2013) · 2013
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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. (2016) · 2016
Cited alongside, same era.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
Benhenda, M. (2017) · 2017
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Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Cunha Farias, P. L., and Aspuru-Guzik, A. (2017) · 2017
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., and Chen, H. (2017) · 2017
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Deep reinforcement learning for de-novo drug design
Popova, M., Isayev, O., and Tropsha, A. (2017) · 2017
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Segler, M. H., Kogej, T., Tyrchan, C., and Waller, M. P. (2017) · 2017
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PubChem BioAssay: 2017 update
Wang, Y., Bryant, S. H., Cheng, T., Wang, J., Gindulyte, A., Shoemaker, B. A., Thiessen, P. A., He, S., and Zhang, J. (2016) · 2017
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ChemTS: an efficient python library for de novo molecular generation
Yang, X., Zhang, J., Yoshizoe, K., Terayama, K., and Tsuda, K. (2017) · 2017
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
Cited alongside, same era.
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
Jaques, N., Gu, S., Bahdanau, D., Hernández-Lobato, J. M., Turner, R. E., and Eck, D. (2017) · 2017
Cited alongside, same era.
Self-normalizing neural networks
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017) · 2017
Cited alongside, same era.
Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J. (2017) · 2017
Cited alongside, same era.
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P. (2018) · 2018
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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J., Ceulemans, H., Clevert, D.-A., and Hochreiter, S. (2018) · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N. (2018) · 2018
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Coulomb GANs: Provably optimal nash equilibria via potential fields
Unterthiner, T., Nessler, B., Klambauer, G., Heusel, M., Ramsauer, H., and Hochreiter, S. (2018) · 2018
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