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Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
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Molecular structure description
L. B. Kier, L. H. Hall, et al · 1999
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Frontier orbital energies, hydrophobicity and steric factors as physical qsar descriptors of molecular mutagenicity. a review with a case study: Mx compounds
K. Tuppurainen · 1999
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Smarts theory. daylight theory manual, 2000
C. James, D. Weininger, and J. Delany · 2000
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Synopsis: synthesize and optimize system in silico
H. M. Vinkers, M. R. de Jonge, F. F. Daeyaert, J. Heeres, L. M. Koymans, J. H. van Lenthe, P. J. Lewi, H. Timmerman, K. Van Aken, and P. A. Janssen · 2003
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A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules
N. Brown, B. McKay, F. Gilardoni, and J. Gasteiger · 2004
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Similarity to molecules in the training set is a good discriminator for prediction accuracy in qsar
R. P. Sheridan, B. P. Feuston, V. N. Maiorov, and S. K. Kearsley · 2004
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
P. Ertl and A. Schuffenhauer · 2009
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Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
J. Law, Z. Zsoldos, A. Simon, D. Reid, Y. Liu, S. Y. Khew, A. P. Johnson, S. Major, R. A. Wade, and H. Y. Ando · 2009
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Knowledge-based approach to de novo design using reaction vectors
H. Patel, M. J. Bodkin, B. Chen, and V. J. Gillet · 2009
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Extended-connectivity fingerprints
D. Rogers and M. Hahn · 2010
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Applicability domains for classification problems: benchmarking of distance to models for ames mutagenicity set
I. Sushko, S. Novotarskyi, R. Körner, A. K. Pandey, A. Cherkasov, J. Li, P. Gramatica, K. Hansen, T. Schroeter, K.-R. Müller, et al · 2010
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Best practices for qsar model development, validation, and exploitation
A. Tropsha · 2010
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Hiv-1 antiretroviral drug therapy
E. J. Arts and D. J. Hazuda · 2012
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Quantifying the chemical beauty of drugs
G. R. Bickerton, G. V. Paolini, J. Besnard, S. Muresan, and A. L. Hopkins · 2012
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Dogs: reaction-driven de novo design of bioactive compounds
M. Hartenfeller, H. Zettl, M. Walter, M. Rupp, F. Reisen, E. Proschak, S. Weggen, H. Stark, and G. Schneider · 2012
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Qsar modeling: where have you been? where are you going to?
A. Cherkasov, E. N. Muratov, D. Fourches, A. Varnek, I. I. Baskin, M. Cronin, J. Dearden, P. Gramatica, Y. C. Martin, R. Todeschini, et al · 2014
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Deterministic policy gradient algorithms
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller · 2014
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
D. Bajusz, A. Rácz, and K. Héberger · 2015
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Reinforcement learning in large discrete action spaces
G. Dulac-Arnold, R. Evans, P. Sunehag, and B. Coppin · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. M. O. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Rdkit: Open-source cheminformatics software
G. Landrum · 2016
Cited alongside, same era.
Computer-Assisted Synthetic Planning: The End of the Beginning
S. Szymkuc, E. P. Gajewska, T. Klucznik, K. Molga, P. Dittwald, M. Startek, M. Bajczyk, and B. A. Grzybowski · 2016
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Thinking fast and slow with deep learning and tree search
T. Anthony, Z. Tian, and D. Barber · 2017
Cited alongside, same era.
The chembl database in 2017
A. Gaulton, A. Hersey, M. Nowotka, A. P. Bento, J. Chambers, D. Mendez, P. Mutowo, F. Atkinson, L. J. Bellis, E. Cibrián-Uhalte, et al · 2017
Cited alongside, same era.
Deep reinforcement learning for de novo drug design
M. Popova, O. Isayev, and A. Tropsha · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
K. Preuer, P. Renz, T. Unterthiner, S. Hochreiter, and G. Klambauer · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
B. Sanchez-Lengeling and A. Aspuru-Guzik · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
M. H. S. Segler, M. Preuss, and M. P. Waller · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
M. Simonovsky and N. Komodakis · 2018
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Deep pepper: Expert iteration based chess agent in the reinforcement learning setting
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Deep learning for computational chemistry
G. B. Goh, N. O. Hodas, and A. Vishnu · 2017
Cited alongside, same era.
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
G. L. Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. L. C. Farias, and A. Aspuru-Guzik · 2017
Cited alongside, same era.
Lightgbm: A highly efficient gradient boosting decision tree
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu · 2017
Cited alongside, same era.
Molecular de-novo design through deep reinforcement learning
M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen · 2017
Cited alongside, same era.
Automating drug discovery
G. Schneider · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Cited alongside, same era.
Generating focused molecule libraries for drug discovery with recurrent neural networks
M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller · 2017
Cited alongside, same era.
S. K. G. V., K. Goyette, A. Chamseddine, and B. Considine · 2018
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Virtual Chemical Libraries
W. P. Walters · 2018
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Optimization of molecules via deep reinforcement learning
Z. Zhou, S. M. Kearnes, L. Li, R. N. Zare, and P. Riley · 2018
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A model to search for synthesizable molecules
J. Bradshaw, B. Paige, M. J. Kusner, M. H. S. Segler, and J. M. Hernández-Lobato · 2019
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Guacamol: benchmarking models for de novo molecular design
N. Brown, M. Fiscato, M. H. Segler, and A. C. Vaucher · 2019
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Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
A. Button, D. Merk, J. A. Hiss, and G. Schneider · 2019
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Deep learning for molecular design—a review of the state of the art
D. C. Elton, Z. Boukouvalas, M. D. Fuge, and P. W. Chung · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
J. H. Jensen · 2019
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Reaction-based enumeration, active learning, and free energy calculations to rapidly explore synthetically tractable chemical space and optimize potency of cyclin-dependent kinase 2 inhibitors
K. D. Konze, P. H. Bos, M. K. Dahlgren, K. Leswing, I. Tubert-Brohman, A. Bortolato, B. Robbason, R. Abel, and S. Bhat · 2019
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M. Krenn, F. Häse, A. Nigam, P. Friederich, and A. Aspuru-Guzik · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
S. Liu, M. F. Demirel, and Y. Liang · 2019
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Augmenting genetic algorithms with deep neural networks for exploring the chemical space
A. Nigam, P. Friederich, M. Krenn, and A. Aspuru-Guzik · 2019
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From target to drug: Generative modeling for the multimodal structure-based ligand design
M. Skalic, D. Sabbadin, B. Sattarov, S. Sciabola, and G. De Fabritiis · 2019
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Efficient multi-objective molecular optimization in a continuous latent space
R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noé, and D.-A. Clevert · 2019
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The synthesizability of molecules proposed by generative models
W. Gao and C. W. Coley · 2020
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Qsar without borders
E. N. Muratov, J. Bajorath, R. P. Sheridan, I. V. Tetko, D. Filimonov, V. Poroikov, T. I. Oprea, I. I. Baskin, A. Varnek, A. Roitberg, et al · 2020
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Assessing the impact of generative ai on medicinal chemistry
W. P. Walters and M. Murcko · 2020
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