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The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support for chemical and drug design.
Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 1905
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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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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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ZINC: A free tool to discover chemistry for biology
John J. Irwin, Teague Sterling, Michael M. Mysinger, Erin S. Bolstad, and Ryan G. Coleman · 2012
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Rdkit documentation
Greg Landrum · 2013
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Estimation of the size of drug-like chemical space based on GDB-17 data
Pavel G. Polishchuk, Timur I. Madzhidov, and Alexandre Varnek · 2013
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ZINC 15 - ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 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 S. Jaakkola · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt · 2018
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Constrained generation of semantically valid graphs via regularizing variational autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
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Molecular sets (MOSES): A benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamín Sánchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Sergey I. Nikolenko, Alán Aspuru-Guzik, and Alex Zhavoronkov · 2018
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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay S. Pande, and Jure Leskovec · 2018
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Learning multimodal graph-to-graph translation for molecule optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi S. Jaakkola · 2019
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Virtual compound libraries in computer-assisted drug discovery
Niek van Hilten, Florent Chevillard, and Peter Kolb · 2019
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Efficient multi-objective molecular optimization in a continuous latent space
Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert · 2019
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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Guiding deep molecular optimization with genetic exploration
Sungsoo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin · 2020
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Hierarchical generation of molecular graphs using structural motifs
Bartsmiles: Generative masked language models for molecular representations
Gayane Chilingaryan, Hovhannes Tamoyan, Ani Tevosyan, Nelly Babayan, Lusine Khondkaryan, Karen Hambardzumyan, Zaven Navoyan, Hrant Khachatrian, and Armen Aghajanyan · 2022
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LIMO: latent inceptionism for targeted molecule generation
Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, and Rose Yu · 2022
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Translation between molecules and natural language
Carl Edwards, Tuan Manh Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji · 2022
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Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2022
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2022
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Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2020
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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alán Aspuru-Guzik · 2020
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BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan T. McDonald · 2020
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Augmenting genetic algorithms with deep neural networks for exploring the chemical space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn, and Alán Aspuru-Guzik · 2020
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A graph to graphs framework for retrosynthesis prediction
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, and Jian Tang · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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Chemformer: a pre-trained transformer for computational chemistry
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum · 2022
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SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew D. White, Adamo Young, Rose Yu, and Alán Aspuru-Guzik · 2022
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BRIO: bringing order to abstractive summarization
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Unipelt: A unified framework for parameter-efficient language model tuning
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Large-scale chemical language representations capture molecular structure and properties
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A molecular multimodal foundation model associating molecule graphs with natural language
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Molsearch: Search-based multi-objective molecular generation and property optimization
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Deep learning approaches for de novo drug design: An overview
Mingyang Wang, Zhe Wang, Huiyong Sun, Jike Wang, Chao Shen, Gaoqi Weng, Xin Chai, Honglin Li, Dongsheng Cao, and Tingjun Hou · 2022
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Regression transformer enables concurrent sequence regression and generation for molecular language modelling
Jannis Born and Matteo Manica · 2023
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Large language model for molecular chemistry
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A survey of hallucination in large foundation models
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Genetic algorithms are strong baselines for molecule generation
Austin Tripp and José Miguel Hernández-Lobato · 2023
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Retrieval-based controllable molecule generation
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Cognitive mirage: A review of hallucinations in large language models
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Siren’s song in the AI ocean: A survey on hallucination in large language models
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NPASS database update 2023: quantitative natural product activity and species source database for biomedical research
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Mol-instructions: A large-scale biomolecular instruction dataset for large language models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2024
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