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De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles.
Kullback, S.; Leibler, R. A. On information and sufficiency. Ann. Math. Stat. 1951
1951
Earlier work this paper cites.
Hansch, C.; Fujita, T. p- σ \sigma - π \pi Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 1964
1964
Earlier work this paper cites.
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 1988
1988
Earlier work this paper cites.
Baskin, I. I.; Gordeeva, E. V.; Devdariani, R. O.; Zefirov, N. S.; Paliulin, V. A.; Stankevich, M. I. Methodology of the Inverse Problem Solution for the Structure Property Relation in Case of Topological Indexes. Dokl. Akad. Nauk SSSR 1989
1989
Earlier work this paper cites.
Böhm, H.-J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput.-Aided Mol. Des. 1992
1992
Earlier work this paper cites.
Gillet, V. J.; Newell, W.; Mata, P.; Myatt, G.; Sike, S.; Zsoldos, Z.; Johnson, A. P. SPROUT: recent developments in the de novo design of molecules. J. Chem. Inf. Comput. Sci 1994
1994
Earlier work this paper cites.
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural computation 1997
1997
Earlier work this paper cites.
Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997
1997
Earlier work this paper cites.
Willett, P.; Barnard, J. M.; Downs, G. M. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 1998
1998
Earlier work this paper cites.
Wang, R.; Gao, Y.; Lai, L. LigBuilder: a multi-purpose program for structure-based drug design. Molecular modeling annual 2000
2000
Earlier work this paper cites.
Ertl, P.; Rohde, B.; Selzer, P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J. Med. Chem. 2000
2000
Earlier work this paper cites.
Rarey, M.; Stahl, M. Similarity searching in large combinatorial chemistry spaces. J. Comput.-Aided Mol. Des. 2001
2001
Earlier work this paper cites.
Brown, N.; McKay, B.; Gilardoni, F.; Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 2004
2004
Earlier work this paper cites.
Lajiness, M. S.; Maggiora, G. M.; Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 2004
2004
Earlier work this paper cites.
Irwin, J. J.; Shoichet, B. K. ZINC- A free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005
2005
Earlier work this paper cites.
Gasteiger, J.; Engel, T. Chemoinformatics: a textbook ; John Wiley & Sons, 2006
2006
Earlier work this paper cites.
Pärn, J.; Degen, J.; Rarey, M. Exploring fragment spaces under multiple physicochemical constraints. J. Comput.-Aided Mol. Des. 2007
2007
Earlier work this paper cites.
Gasteiger, J. De novo design and synthetic accessibility. J. Comput.-Aided Mol. Des. 2007
2007
Earlier work this paper cites.
Degen, J.; Wegscheid-Gerlach, C.; Zaliani, A.; Rarey, M. On the Art of Compiling and Using’Drug-Like’Chemical Fragment Spaces. ChemMedChem 2008
2008
Earlier work this paper cites.
Zaliani, A.; Boda, K.; Seidel, T.; Herwig, A.; Schwab, C. H.; Gasteiger, J.; Claußen, H.; Lemmen, C.; Degen, J.; Pärn, J.; Rarey, M. Second-generation de novo design: a view from a medicinal chemist perspective. J. Comput.-Aided Mol. Des. 2009
2009
Earlier work this paper cites.
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Fei-Fei, L. Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition. 2009; pp 248–255
2009
Earlier work this paper cites.
Nicolaou, C. A.; Apostolakis, J.; Pattichis, C. S. De novo drug design using multiobjective evolutionary graphs. J. Chem. Inf. Model. 2009
2009
Earlier work this paper cites.
Hartenfeller, M.; Schneider, G. Enabling future drug discovery by de novo design. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2011
2011
Earlier work this paper cites.
Ruddigkeit, L.; Van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 2012
2012
Earlier work this paper cites.
Ertl, P.; Lewis, R. IADE: a system for intelligent automatic design of bioisosteric analogs. J. Comput.-Aided Mol. Des. 2012
2012
Earlier work this paper cites.
Bickerton, G. R.; Paolini, G. V.; Besnard, J.; Muresan, S.; Hopkins, A. L. Quantifying the chemical beauty of drugs. Nat. Chem. 2012
2012
Earlier work this paper cites.
Nicolaou, C. A.; Brown, N. Multi-objective optimization methods in drug design. Drug Discovery Today: Technol. 2013
2013
Earlier work this paper cites.
Schneider, G. De novo molecular design ; John Wiley & Sons, 2013
2013
Earlier work this paper cites.
Lauck, F.; Rarey, M. In De novo Molecular Design ; Schneider, G., Ed.; Wiley Online Library, 2013; pp 325–347
2013
Earlier work this paper cites.
Ramakrishnan, R.; Dral, P. O.; Rupp, M.; Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014
2014
Earlier work this paper cites.
Chevillard, F.; Kolb, P. SCUBIDOO: a large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability. J. Chem. Inf. Model. 2015
2015
Cited alongside, same era.
Schmidhuber, J. Deep learning in neural networks: An overview. Neural networks 2015
2015
Cited alongside, same era.
Schneider, N.; Sayle, R. A.; Landrum, G. A. Get Your Atoms in Order — An Open-Source Implementation of a Novel and Robust Molecular Canonicalization Algorithm. J. Chem. Inf. Model. 2015
2015
Cited alongside, same era.
Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 2016
2016
Cited alongside, same era.
2018
Closest in time.
Kajino, H. Molecular Hypergraph Grammar with its Application to Molecular Optimization. 2018
2018
Closest in time.
Lim, J.; Ryu, S.; Kim, J. W.; Kim, W. Y. Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminf. 2018
2018
Closest in time.
Neil, D.; Segler, M.; Guasch, L.; Ahmed, M.; Plumbley, D.; Sellwood, M.; Brown, N. Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. ICLR 2018 Conference. 2018
2018
Closest in time.
Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 2018
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2016
Cited alongside, same era.
Kadurin, A.; Aliper, A.; Kazennov, A.; Mamoshina, P.; Vanhaelen, Q.; Khrabrov, K.; Zhavoronkov, A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 2016
2016
Cited alongside, same era.
Wager, T. T.; Hou, X.; Verhoest, P. R.; Villalobos, A. Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery. ACS Chem. Neurosci. 2016
2016
Cited alongside, same era.
Valeur, E.; Guéret, S. M.; Adihou, H.; Gopalakrishnan, R.; Lemurell, M.; Waldmann, H.; Grossmann, T. N.; Plowright, A. T. New modalities for challenging targets in drug discovery. Angew. Chem. Int. Ed. 2017
2017
Cited alongside, same era.
Kusner, M. J.; Paige, B.; Hernández-Lobato, J. M. Grammar Variational Autoencoder. 2017
2017
Cited alongside, same era.
Yuan, W.; Jiang, D.; Nambiar, D. K.; Liew, L. P.; Hay, M. P.; Bloomstein, J.; Lu, P.; Turner, B.; Le, Q.-T.; Tibshirani, R.; Khatri, P.; Moloney, M. G.; Koong, A. C. Chemical Space Mimicry for Drug Discovery. J. Chem. Inf. Model. 2017
2017
Cited alongside, same era.
Bjerrum, E. J.; Threlfall, R. Molecular Generation with Recurrent Neural Networks (RNNs). 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Closest in time.
2018
Closest in time.
Li, Y.; Zhang, L.; Liu, Z. Multi-objective de novo drug design with conditional graph generative model. J. Cheminf. 2018
2018
Closest in time.
Li, Y.; Zhou, X.; Liu, Z.; Zhang, L. Designing natural product-like virtual libraries using deep molecule generative models. J. Chin. Pharm. Sci. 2018
2018
Closest in time.
Putin, E.; Asadulaev, A.; Ivanenkov, Y.; Aladinskiy, V.; Sanchez-Lengeling, B.; Aspuru-Guzik, A.; Zhavoronkov, A. Reinforced Adversarial Neural Computer for de Novo
2018
Closest in time.
De Cao, N.; Kipf, T. MolGAN: An implicit generative model for small molecular graphs. 2018
2018
Closest in time.
Putin, E.; Asadulaev, A.; Vanhaelen, Q.; Ivanenkov, Y.; Aladinskaya, A. V.; Aliper, A.; Zhavoronkov, A. Adversarial Threshold Neural Computer for Molecular de Novo Design. Mol. Pharmaceutics 2018
2018
Closest in time.
Polykovskiy, D.; Zhebrak, A.; Vetrov, D.; Ivanenkov, Y.; Aladinskiy, V.; Mamoshina, P.; Bozdaganyan, M.; Aliper, A.; Zhavoronkov, A.; Kadurin, A. Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. Mol. Pharmaceutics 2018
2018
Closest in time.
2018
Closest in time.
Merk, D.; Grisoni, F.; Friedrich, L.; Schneider, G. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators. Nat. Commun. Chem. 2018
2018
Closest in time.
Merk, D.; Friedrich, L.; Grisoni, F.; Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inf. 2018
2018
Closest in time.
Arús-Pous, J.; Blaschke, T.; Reymond, J.-L.; Chen, H.; Engkvist, O. Exploring the GDB-13 Chemical Space Using Deep Generative Models. 2018
2018
Closest in time.
Mendez, D.; Gaulton, A.; Bento, A. P.; Chambers, J.; De Veij, M.; Félix, E.; Magariños, M. P.; Mosquera, J. F.; Mutowo, P.; Nowotka, M.; Gordillo-Marañón, M.; Hunter, F.; Junco, L.; Mugumbate, G.; Rodriguez-Lopez, M.; Atkinson, F.; Bosc, N.; Radoux, C. J.; Segura-Cabrera, A.; Hersey, A.; Leach, A. R. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2018
2018
Closest in time.
Preuer, K.; Renz, P.; Unterthiner, T.; Hochreiter, S.; Klambauer, G. Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery. J. Chem. Inf. Model. 2018
2018
Closest in time.
GuacaMol Package. https://github.com/benevolentAI/guacamol , Accessed: November 19, 2018
2018
Closest in time.
GuacaMol baselines repository. https://github.com/benevolentAI/guacamol_baselines , Accessed: November 19, 2018
2018
Closest in time.
Tevet, G.; Habib, G.; Shwartz, V.; Berant, J. Evaluating Text GANs as Language Models. 2018
2018
Closest in time.
Post-processed ChEMBL datasets. https://figshare.com/projects/GuacaMol/56639 , Accessed: November 20, 2018
2018
Closest in time.
FCD Package. https://github.com/bioinf-jku/FCD , Accessed: November 15, 2018
2018
Closest in time.
2018
Closest in time.
Jensen, J. H. Graph-based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space. 2018
2018
Closest in time.
2018
Closest in time.
Walters, P. rd filters. https://github.com/PatWalters/rd_filters , Accessed: January 14, 2019
2019
Closest in time.
Landrum, G. RDKit blog. http://rdkit.blogspot.com/2013/10/fingerprint-thresholds.html , Accessed: January 14, 2019
2019
Closest in time.
MOSES repository. https://github.com/molecularsets/moses , Accessed: January 14, 2019
2019
Closest in time.