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Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models.
Structural determination of paraffin boiling points
Harry Wiener · 1947
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Molecular structure: Property relationships
Paul G Seybold, Michael May, and Ujjvala A Bagal · 1987
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A graph-theoretical approach to structure-property relationships
Zlatko Mihalić and Nenad Trinajstić · 1992
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Comparison of two methods for predicting aqueous solubility
Debra L Peterson and Samuel H Yalkowsky · 2001
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Chemical process safety: fundamentals with applications
Daniel A Crowl and Joseph F Louvar · 2001
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Reoptimization of mdl keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse · 2002
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A review of estimation methods for flash points and flammability limits
M Vidal, WJ Rogers, JC Holste, and MS Mannan · 2004
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Artificial intelligence approaches for rational drug design and discovery
Wlodzislaw Duch, Karthikeyan Swaminathan, and Jaroslaw Meller · 2007
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Flexible porous metal-organic frameworks for a controlled drug delivery
Patricia Horcajada, Christian Serre, Guillaume Maurin, Naseem A Ramsahye, Francisco Balas, María Vallet-Regí, Muriel Sebban, Francis Taulelle, and Gérard Férey · 2008
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Metal–organic framework materials as catalysts
JeongYong Lee, Omar K Farha, John Roberts, Karl A Scheidt, SonBinh T Nguyen, and Joseph T Hupp · 2009
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Prediction of upper flammability limit percent of pure compounds from their molecular structures
Farhad Gharagheizi · 2009
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Prediction of the upper flammability limits of organic compounds from molecular structures
Yong Pan, Juncheng Jiang, Rui Wang, Hongyin Cao, and Yi Cui · 2009
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Efficient calculation of diffusion limitations in metal organic framework materials: a tool for identifying materials for kinetic separations
Emmanuel Haldoupis, Sankar Nair, and David S Sholl · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Metal–organic frameworks in biomedicine
Patricia Horcajada, Ruxandra Gref, Tarek Baati, Phoebe K Allan, Guillaume Maurin, Patrick Couvreur, Gérard Férey, Russell E Morris, and Christian Serre · 2012
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Chapter 8 - hazard identification
Sam Mannan · 2012
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The influence of bioisosteres in drug design: tactical applications to address developability problems
Nicholas A Meanwell · 2015
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Deep learning in drug discovery
Erik Gawehn, Jan A Hiss, and Gisbert Schneider · 2016
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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The role of metal–organic frameworks in a carbon-neutral energy cycle
Alexander Schoedel, Zhe Ji, and Omar M Yaghi · 2016
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Imidazolium ionic liquids, imidazolylidene heterocyclic carbenes, and zeolitic imidazolate frameworks for co2 capture and photochemical reduction
Sibo Wang and Xinchen Wang · 2016
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Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs
Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov · 2016
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Rdkit: open-source cheminformatics http://www. rdkit. org
G Landrum · 2016
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Prediction errors of molecular machine learning models lower than hybrid dft error
Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, and O. Anatole von Lilienfeld · 2017
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Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Classification of the covid-19 infected patients using densenet201 based deep transfer learning
Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar, and Manjit Kaur · 2021
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Rohan Gupta, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K. Ambasta, and Pravir Kumar · 2021
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Machine learning in combinatorial polymer chemistry
Adam J Gormley and Michael A Webb · 2021
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Computational sustainability meets materials science
Carla P Gomes, Daniel Fink, R Bruce Van Dover, and John M Gregoire · 2021
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Generating visual explanations with natural language
Lisa Anne Hendricks, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Zeynep Akata · 2021
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An integrative machine learning approach for prediction of toxicity-related drug safety
Artem Lysenko, Alok Sharma, Keith A Boroevich, and Tatsuhiko Tsunoda · 2018
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Machine learning interatomic potentials as emerging tools for materials science
Volker L. Deringer, Miguel A. Caro, and Gábor Csányi · 2019
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Definitions, methods, and applications in interpretable machine learning
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Definitions, methods, and applications in interpretable machine learning
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Learning to explain with complemental examples
Atsushi Kanehira and Tatsuya Harada · 2019
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Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
Robert P Sheridan · 2019
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Ting-Hsiang Hung, Qiang Lyu, Li-Chiang Lin, and Dun-Yen Kang · 2021
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Machine learning in drug discovery: A review
Suresh Dara, Swetha Dhamercherla, Surender Singh Jadav, CH Madhu Babu, Mohamed Jawed Ahsan, Suresh Dara darasuresh, and S Dara · 2022
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Model agnostic generation of counterfactual explanations for molecules
Geemi P. Wellawatte, Aditi Seshadri, and Andrew D. White · 2022
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Why does that molecule smell?
Aditi Seshadri, Heta A. Gandhi, Geemi P. Wellawatte, and Andrew D. White · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors
Sherif Abdulkader Tawfik and Salvy P Russo · 2022
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Analyzing acetylene adsorption of metal–organic frameworks based on machine learning
Peisong Yang, Gang Lu, Qingyuan Yang, Lei Liu, Xin Lai, and Duli Yu · 2022
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Alphafold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel cdk20 small molecule inhibitor
Feng Ren, Xiao Ding, Min Zheng, Mikhail Korzinkin, Xin Cai, Wei Zhu, Alexey Mantsyzov, Alex Aliper, Vladimir Aladinskiy, Zhongying Cao, et al · 2023
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A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
Gesina Schwalbe and Bettina Finzel · 2023
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A perspective on explanations of molecular prediction models
Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White · 2023
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A survey on xai and natural language explanations
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, and Navid Nobani · 2023
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The future of chemistry is language
Andrew D White · 2023
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Leveraging large language models for predictive chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit · 2023
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Chemcrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller · 2023
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Gpt-4technicalreport
OpenAI · 2023
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Multi-order graph attention network for water solubility prediction and interpretation
Sangho Lee, Hyunwoo Park, Chihyeon Choi, Wonjoon Kim, Ki Kang Kim, Young-Kyu Han, Joohoon Kang, Chang-Jong Kang, and Youngdoo Son · 2023
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