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Recent breakthroughs in machine learning and artificial intelligence, fueled by scientific data, are revolutionizing the discovery of new materials.
Prediction of critical micelle concentration using a quantitative structure–property relationship approach
Paul DT Huibers, Victor S Lobanov, AR Katritzky, DO Shah, and M Karelson · 1997
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The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein · 1998
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Quantum chemical models (nobel lecture)
John A Pople · 1999
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Estimation of critical micelle concentration of anionic surfactants with qspr approach
Xuefeng Li, Gaoyong Zhang, Jinfeng Dong, Xiaohai Zhou, Xiaoci Yan, and Mingdao Luo · 2004
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Correlation of critical micelle concentration of sodium alkyl benzenesulfonates with molecular descriptors
Li Xuefeng, Zhang Gaoyong, Dong Jinfeng, Zhou Xiaohai, Yan Xiaoci, and Luo Mingdao · 2006
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Smarts-a language for describing molecular patterns, 2007
Inc. Daylight Chemical Information Systems · 2007
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott and Arthur J Olson · 2010
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Prediction of critical micelle concentration of cationic surfactants using connectivity indices
Anna Mozrzymas and Bożenna Różycka-Roszak · 2011
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Reactive search optimization; application to multiobjective optimization problems
Amir Mosavi and Atieh Vaezipour · 2012
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From the computer to the laboratory: materials discovery and design using first-principles calculations
Geoffroy Hautier, Anubhav Jain, and Shyue Ping Ong · 2012
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Large-scale screening of hypothetical metal–organic frameworks
Christopher E Wilmer, Michael Leaf, Chang Yeon Lee, Omar K Farha, Brad G Hauser, Joseph T Hupp, and Randall Q Snurr · 2012
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The plasticity of wdr5 peptide-binding cleft enables the binding of the set1 family of histone methyltransferases
Pamela Zhang, Hwabin Lee, Joseph S Brunzelle, and Jean-Francois Couture · 2012
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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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Informatics for materials science and engineering, 2013
S Samudrala, K Rajan, and B Ganapathysubramanian · 2013
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Small-molecule inhibition of mll activity by disruption of its interaction with wdr5
Guillermo Senisterra, Hong Wu, Abdellah Allali-Hassani, Gregory A Wasney, Dalia Barsyte-Lovejoy, Ludmila Dombrovski, Aiping Dong, Kong T Nguyen, David Smil, Yuri Bolshan, et al · 2013
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Rdkit documentation
Greg Landrum · 2013
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Pseudopotentials for high-throughput dft calculations
Kevin F Garrity, Joseph W Bennett, Karin M Rabe, and David Vanderbilt · 2014
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What is high-throughput virtual screening? a perspective from organic materials discovery
Edward O Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
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A review on natural surfactants
Sourav De, Susanta Malik, Aniruddha Ghosh, Rumpa Saha, and Bidyut Saha · 2015
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Surfactants at the design limit
Adam Czajka, Gavin Hazell, and Julian Eastoe · 2015
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Pharmacological targeting of the wdr5-mll interaction in c/ebp α \alpha n-terminal leukemia
Florian Grebien, Masoud Vedadi, Matthäus Getlik, Roberto Giambruno, Amit Grover, Roberto Avellino, Anna Skucha, Sarah Vittori, Ekaterina Kuznetsova, David Smil, et al · 2015
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Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal and Alok Choudhary · 2016
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New qspr models to predict the critical micelle concentration of sugar-based surfactants
Theophile Gaudin, Patricia Rotureau, Isabelle Pezron, and Guillaume Fayet · 2016
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Structure-based optimization of a small molecule antagonist of the interaction between wd repeat-containing protein 5 (wdr5) and mixed-lineage leukemia 1 (mll1)
Matthaus Getlik, David Smil, Carlos Zepeda-Velazquez, Yuri Bolshan, Gennady Poda, Hong Wu, Aiping Dong, Ekaterina Kuznetsova, Richard Marcellus, Guillermo Senisterra, et al · 2016
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High-affinity small molecular blockers of mixed lineage leukemia 1 (mll1)-wdr5 interaction inhibit mll1 complex h3k4 methyltransferase activity
Dong-Dong Li, Wei-Lin Chen, Zhi-Hui Wang, Yi-Yue Xie, Xiao-Li Xu, Zheng-Yu Jiang, Xiao-Jin Zhang, Qi-Dong You, and Xiao-Ke Guo · 2016
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The chemistry of metal–organic frameworks for co2 capture, regeneration and conversion
Christopher A Trickett, Aasif Helal, Bassem A Al-Maythalony, Zain H Yamani, Kyle E Cordova, and Omar M Yaghi · 2017
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Rational design of a low-cost, high-performance metal–organic framework for hydrogen storage and carbon capture
Witman Matthew, Ling Sanliang, Gladysiak Andrzej, Smit Berend, Slater Ben, Haranczyk Maciej, et al · 2017
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Design, synthesis, and initial evaluation of affinity-based small molecular probe for detection of wdr5
Wei-Lin Chen, Dong-Dong Li, Zhi-Hui Wang, Xiao-Li Xu, Xiao-Jin Zhang, Zheng-Yu Jiang, Xiao-Ke Guo, and Qi-Dong You · 2018
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Recent advances in gas storage and separation using metal–organic frameworks
Hao Li, Kecheng Wang, Yujia Sun, Christina T Lollar, Jialuo Li, and Hong-Cai Zhou · 2018
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Secondary building units as the turning point in the development of the reticular chemistry of mofs
Markus J Kalmutzki, Nikita Hanikel, and Omar M Yaghi · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Cited alongside, same era.
Displacement of wdr5 from chromatin by a win site inhibitor with picomolar affinity
Erin R Aho, Jing Wang, Rocco D Gogliotti, Gregory C Howard, Jason Phan, Pankaj Acharya, Jonathan D Macdonald, Ken Cheng, Shelly L Lorey, Bin Lu, et al · 2019
Cited alongside, same era.
Bigsmiles: a structurally-based line notation for describing macromolecules
Tzyy-Shyang Lin, Connor W Coley, Hidenobu Mochigase, Haley K Beech, Wencong Wang, Zi Wang, Eliot Woods, Stephen L Craig, Jeremiah A Johnson, Julia A Kalow, et al · 2019
Cited alongside, same era.
Surfactant self-assembling and critical micelle concentration: one approach fits all?
Diego Romano Perinelli, Marco Cespi, Nicola Lorusso, Giovanni Filippo Palmieri, Giulia Bonacucina, and Paolo Blasi · 2020
Cited alongside, same era.
Targeting wd repeat-containing protein 5 (wdr5): a medicinal chemistry perspective
Xin Chen, Junjie Xu, Xianghan Wang, Guanlu Long, Qidong You, and Xiaoke Guo · 2021
Opportunities for retrieval and tool augmented large language models in scientific facilities
Michael H Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K Sastry, Matthew T Dearing, Ross J Harder, Rama K Vasudevan, and Mathew J Cherukara · 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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Analyzing the accuracy of critical micelle concentration predictions using deep learning
Alexander Moriarty, Takeshi Kobayashi, Matteo Salvalaglio, Panagiota Angeli, Alberto Striolo, and Ian McRobbie · 2023
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Structure-based discovery of potent wd repeat domain 5 inhibitors that demonstrate efficacy and safety in preclinical animal models
Kevin B Teuscher, Somenath Chowdhury, Kenneth M Meyers, Jianhua Tian, Jiqing Sai, Mayme Van Meveren, Taylor M South, John L Sensintaffar, Tyson A Rietz, Soumita Goswami, et al · 2023
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A practical guide to large-scale docking
Brian J Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase M Webb, Reed M Stein, Elissa A Fink, Trent E Balius, Jens Carlsson, John J Irwin, et al · 2021
Cited alongside, same era.
Recent advances on preparation and environmental applications of mof-derived carbons in catalysis
Mengjie Hao, Muqing Qiu, Hui Yang, Baowei Hu, and Xiangxue Wang · 2021
Cited alongside, same era.
Metal–organic frameworks for drug delivery: a design perspective
Harrison D Lawson, S Patrick Walton, and Christina Chan · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
Cited alongside, same era.
Predicting critical micelle concentrations for surfactants using graph convolutional neural networks
Shiyi Qin, Tianyi Jin, Reid C Van Lehn, and Victor M Zavala · 2021
Cited alongside, same era.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Cited alongside, same era.
A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
Cited alongside, same era.
Moformer: self-supervised transformer model for metal–organic framework property prediction
Zhonglin Cao, Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani · 2023
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Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials
Samantha Stuart, Jeffrey Watchorn, and Frank X Gu · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao · 2023
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Tool learning with foundation models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, et al · 2023
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Learning peptide properties with positive examples only
Mehrad Ansari and Andrew D White · 2024
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Organa: A robotic assistant for automated chemistry experimentation and characterization
Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, et al · 2024
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Augmenting large language models with chemistry tools
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller · 2024
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Explainable synthesizability prediction of inorganic crystal structures using large language models
Seongmin Kim, Joshua Schrier, and Yousung Jung · 2024
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From text to insight: Large language models for materials science data extraction
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T Koch, José A Márquez, and Kevin Maik Jablonka · 2024
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Automated electrosynthesis reaction mining with multimodal large language models (mllms)
Shi Xuan Leong, Sergio Pablo-García, Zijian Zhang, and Alán Aspuru-Guzik · 2024
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Language agents achieve superhuman synthesis of scientific knowledge, 2024
Michael D. Skarlinski, Sam Cox, Jon M. Laurent, James D. Braza, Michaela Hinks, Michael J. Hammerling, Manvitha Ponnapati, Samuel G. Rodriques, and Andrew D. White · 2024
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Empowering biomedical discovery with ai agents
Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, and Marinka Zitnik · 2024
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Xuemei Gu and Mario Krenn · 2024
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Llmatdesign: Autonomous materials discovery with large language models
Shuyi Jia, Chao Zhang, and Victor Fung · 2024
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Cactus: Chemistry agent connecting tool-usage to science
Andrew D McNaughton, Gautham Ramalaxmi, Agustin Kruel, Carter R Knutson, Rohith A Varikoti, and Neeraj Kumar · 2024
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A review of large language models and autonomous agents in chemistry
Mayk Caldas Ramos, Christopher J Collison, and Andrew D White · 2024
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Are large language models superhuman chemists?
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Benedict Emoekabu, Aswanth Krishnan, Mara Wilhelmi, Macjonathan Okereke, Juliane Eberhardt, Amir Mohammad Elahi, Maximilian Greiner, et al · 2024
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Qspr for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms
Nada Boukelkal, Soufiane Rahal, Redha Rebhi, and Mabrouk Hamadache · 2024
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A dual diffusion model enables 3d molecule generation and lead optimization based on target pockets
Lei Huang, Tingyang Xu, Yang Yu, Peilin Zhao, Xingjian Chen, Jing Han, Zhi Xie, Hailong Li, Wenge Zhong, Ka-Chun Wong, et al · 2024
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Pocketgen: Generating full-atom ligand-binding protein pockets
Zaixi Zhang, Wanxiang Shen, Qi Liu, and Marinka Zitnik · 2024
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Accurate structure prediction of biomolecular interactions with alphafold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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Rgfn: Synthesizable molecular generation using gflownets
Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gaiński, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, and Robert A Batey · 2024
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Designing organic bridging linkers of metal–organic frameworks for enhanced carbon dioxide adsorption
Kahkasha Parveen and Srimanta Pakhira · 2024
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Hello gpt-4o, 2024
OpenAI · 2024
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Claude sonnet 3.5
Anthropic · 2024
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