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De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models.
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, and Alex Zhavoronkov · 1949
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
David Weininger · 1988
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Principled hybrids of generative and discriminative models
Julia A Lasserre, Christopher M Bishop, and Thomas P Minka · 2006
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Gaussian error linear units (gelus)
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Derivative-free and blackbox optimization
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Decoupled weight decay regularization
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Molecular De Novo Design through Deep Reinforcement Learning, August 2017
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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In silico generation of novel, drug-like chemical matter using the LSTM neural network, January 2018
Peter Ertl, Richard Lewis, Eric Martin, and Valery Polyakov · 2018
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Exploring deep recurrent models with reinforcement learning for molecule design
Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, and Nathan Brown · 2018
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Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery, August 2018
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, and Koji Tsuda · 2018
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GuacaMol: Benchmarking Models for de Novo Molecular Design
Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher · 2019
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Two decades of blackbox optimization applications
Stéphane Alarie, Charles Audet, Aïmen E. Gheribi, Michael Kokkolaras, and Sébastien Le Digabel · 2021
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Learning to extend molecular scaffolds with structural motifs
Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt · 2021
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Black-box optimization for automated discovery
Kei Terayama, Masato Sumita, Ryo Tamura, and Koji Tsuda · 2021
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Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, and Kevin K. Yang · 2021
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MolGPT: Molecular Generation Using a Transformer-Decoder Model
Viraj Bagal, Rishal Aggarwal, P. K. Vinod, and U. Deva Priyakumar · 2022
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ChEMBL: Towards direct deposition of bioassay data
David Mendez, Anna Gaulton, A. Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F. Mosquera, Prudence Mutowo, Michal Nowotka, María Gordillo-Marañón, Fiona Hunter, Laura Junco, Grace Mugumbate, Milagros Rodriguez-Lopez, Francis Atkinson, Nicolas Bosc, Chris J. Radoux, Aldo Segura-Cabrera, Anne Hersey, and Andrew R. Leach · 2019
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Hybrid models with deep and invertible features
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Automated de novo drug design: are we nearly there yet?
Gisbert Schneider and David E Clark · 2019
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Augmentation is what you need!
Igor V Tetko, Pavel Karpov, Eric Bruno, Talia B Kimber, and Guillaume Godin · 2019
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Bert has a mouth, and it must speak: Bert as a markov random field language model
Alex Wang and Kyunghyun Cho · 2019
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ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery
John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle · 2020
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Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Large-scale chemical language representations capture molecular structure and properties, 2022
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2022
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Chemical language models for de novo drug design: Challenges and opportunities
Francesca Grisoni · 2023
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Magnet: Motif-agnostic generation of molecules from shapes
Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian Theis, and Stephan Günnemann · 2023
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minGPT, September 2023
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Accelerating high-throughput virtual screening through molecular pool-based active learning
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