Zobrazeno 1 - 10
of 2 306
pro vyhledávání: '"applicability domain"'
Autor:
Sergey Sosnin
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-13 (2024)
Abstract The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a
Externí odkaz:
https://doaj.org/article/7d494abc59c643569706c1f19327fdc1
Publikováno v:
ChemElectroChem, Vol 11, Iss 10, Pp n/a-n/a (2024)
Abstract Machine learning has gained considerable attention in the material science domain and helped discover advanced materials for electrochemical applications. Numerous studies have demonstrated its potential to reduce the resources required for
Externí odkaz:
https://doaj.org/article/86aa92029d6e4f2d9ff6880e829662cd
Autor:
Tamara Tal, Oddvar Myhre, Ellen Fritsche, Joëlle Rüegg, Kai Craenen, Kiara Aiello-Holden, Caroline Agrillo, Patrick J. Babin, Beate I. Escher, Hubert Dirven, Kati Hellsten, Kristine Dolva, Ellen Hessel, Harm J. Heusinkveld, Yavor Hadzhiev, Selma Hurem, Karolina Jagiello, Beata Judzinska, Nils Klüver, Anja Knoll-Gellida, Britta A. Kühne, Marcel Leist, Malene Lislien, Jan L. Lyche, Ferenc Müller, John K. Colbourne, Winfried Neuhaus, Giorgia Pallocca, Bettina Seeger, Ilka Scharkin, Stefan Scholz, Ola Spjuth, Monica Torres-Ruiz, Kristina Bartmann
Publikováno v:
Frontiers in Toxicology, Vol 6 (2024)
In the European regulatory context, rodent in vivo studies are the predominant source of neurotoxicity information. Although they form a cornerstone of neurotoxicological assessments, they are costly and the topic of ethical debate. While the public
Externí odkaz:
https://doaj.org/article/84a7889b64d34295a7a44cec97e647b1
Autor:
Efrén Pérez-Santín, Luis de-la-Fuente-Valentín, Mariano González García, Kharla Andreina Segovia Bravo, Fernando Carlos López Hernández, José Ignacio López Sánchez
Publikováno v:
AIMS Mathematics, Vol 8, Iss 11, Pp 27858-27900 (2023)
In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the devel
Externí odkaz:
https://doaj.org/article/49f655e55aa24e5bbe94bd5d60bdf9b2
Autor:
Srijit Seal, Hongbin Yang, Maria-Anna Trapotsi, Satvik Singh, Jordi Carreras-Puigvert, Ola Spjuth, Andreas Bender
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-16 (2023)
Abstract The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed si
Externí odkaz:
https://doaj.org/article/e1f6430fd7444ba5b221a6795ab8bd50
Autor:
Wouter Heyndrickx, Adam Arany, Jaak Simm, Anastasia Pentina, Noé Sturm, Lina Humbeck, Lewis Mervin, Adam Zalewski, Martijn Oldenhof, Peter Schmidtke, Lukas Friedrich, Regis Loeb, Arina Afanasyeva, Ansgar Schuffenhauer, Yves Moreau, Hugo Ceulemans
Publikováno v:
Artificial Intelligence in the Life Sciences, Vol 3, Iss , Pp 100070- (2023)
In a drug discovery setting, pharmaceutical companies own substantial but confidential datasets. The MELLODDY project developed a privacy-preserving federated machine learning solution and deployed it at an unprecedented scale. Each partner built mod
Externí odkaz:
https://doaj.org/article/c22676311fae4ecf96bc8d92b6788cad
Akademický článek
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Autor:
Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin, Da Shi, Brian J. Bennion, Xiaohua Zhang, Felice C. Lightstone
Publikováno v:
Artificial Intelligence Chemistry, Vol 1, Iss 1, Pp 100004- (2023)
Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data dis
Externí odkaz:
https://doaj.org/article/efcc644112614a30945cbc2b09c71d37
Autor:
Yoshihiro Uesawa
Publikováno v:
International Journal of Molecular Sciences, Vol 25, Iss 3, p 1373 (2024)
The Ames/quantitative structure–activity relationship (QSAR) International Challenge Projects, held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allow
Externí odkaz:
https://doaj.org/article/20f34a1d1402430fadd9ab085f3ad8d6
Publikováno v:
Algorithms, Vol 16, Iss 12, p 573 (2023)
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it desc
Externí odkaz:
https://doaj.org/article/a214218bc4b1403d85876d1e645c33b6