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Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable.
Proper efficiencyand the theory of vector optimization
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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Genetic algorithms
John H Holland · 1992
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The art and practice of structure-based drug design: a molecular modeling perspective
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Smarts-a language for describing molecular patterns, 2007
Inc. Daylight Chemical Information Systems · 2007
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Qingfu Zhang and Hui Li · 2007
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Evolving molecules using multi-objective optimization: applying to adme/tox
Sean Ekins, J Dana Honeycutt, and James T Metz · 2010
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G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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Dagmar Stumpfe and Jürgen Bajorath · 2012
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An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints
Kalyanmoy Deb and Himanshu Jain · 2013
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Zinc 15–ligand discovery for everyone
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Chemts: an efficient python library for de novo molecular generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda · 2017
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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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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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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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Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Unsupervised word embeddings capture latent knowledge from materials science literature
Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
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We should at least be able to design molecules that dock well
Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, and Stanislaw Jastrzebski · 2020
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Constrained bayesian optimization for automatic chemical design using variational autoencoders
Ryan-Rhys Griffiths and José Miguel Hernández-Lobato · 2020
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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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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
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Exploring chemical space using natural language processing methodologies for drug discovery
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, and Elif Ozkirimli · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 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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A deep learning approach to antibiotic discovery
Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackermann, et al · 2020
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Moflow: an invertible flow model for generating molecular graphs
Chengxi Zang and Fei Wang · 2020
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Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider · 2021
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Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021
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Differentiable scaffolding tree for molecular optimization
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W Coley, and Jimeng Sun · 2021
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
Wenhao Gao, Rocío Mercado, and Connor W Coley · 2021
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Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
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Augmented memory: Capitalizing on experience replay to accelerate de novo molecular design
Jeff Guo and Philippe Schwaller · 2023
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Pubchem 2023 update
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2023
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Evolution through large models
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O Stanley · 2023
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Tartarus: A benchmarking platform for realistic and practical inverse molecular design
AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca Thiede, Anshul Kundaje, and Alán Aspuru-Guzik · 2023
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Accelerating high-throughput virtual screening through molecular pool-based active learning, chem
DE Graff, EI Shakhnovich, and CW Coley · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf H Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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AkshatKumar Nigam, Robert Pollice, and Alan Aspuru-Guzik · 2021
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A fresh look at de novo molecular design benchmarks
Austin Tripp, Gregor N. C. Simm, and José Miguel Hernández-Lobato · 2021
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Chemspace: Interpretable and interactive chemical space exploration
Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, and Bolei Zhou · 2022
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Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji · 2022
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Reinforced genetic algorithm for structure-based drug design
Tianfan Fu, Wenhao Gao, Connor Coley, and Jimeng Sun · 2022
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BioT5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations
Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan · 2023
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Bayesian optimization of catalysts with in-context learning
Mayk Caldas Ramos, Shane S Michtavy, Marc D Porosoff, and Andrew D White · 2023
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Scibench: Evaluating college-level scientific problem-solving abilities of large language models
Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R Loomba, Shichang Zhang, Yizhou Sun, and Wei Wang · 2023
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The future of chemistry is language
Andrew D White · 2023
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Drugassist: A large language model for molecule optimization
Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, and Xiangxiang Zeng · 2023
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Large language models for chemistry robotics
Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, et al · 2023
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A decision-language model (dlm) for dynamic restless multi-armed bandit tasks in public health
Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja, Dheeraj Nagaraj, and Milind Tambe · 2024
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Organa: A robotic assistant for automated chemistry experimentation and characterization
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Machine learning-aided generative molecular design
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Augmented memory: Sample-efficient generative molecular design with reinforcement learning
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Leveraging large language models for predictive chemistry
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Genetic-guided gflownets for sample efficient molecular optimization
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Eureka: Human-level reward design via coding large language models
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Are large language models superhuman chemists?
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Mathematical discoveries from program search with large language models
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Dymol: Dynamic many-objective molecular optimization with objective decomposition and progressive optimization
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Invalid smiles are beneficial rather than detrimental to chemical language models
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Self-driving laboratories for chemistry and materials science
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Navigating chemical space with latent flows
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Large language models as optimizers
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Sample-efficient multi-objective molecular optimization with gflownets
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