المؤلفون: Carrió Gaspar, Pau

المساهمون: University/Department: Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut

Thesis Advisors: Pastor Maeso, Manuel

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: In silico prediction, Applicability domain, Read across, QSAR, Toxicity, Prediccions in silico, Domini d'aplicabilitat, Toxicitat

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/328418

المؤلفون: Sergey Sosnin

المصدر: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-13 (2024)

مصطلحات موضوعية: Chemical space visualization, Clustering, Applicability domain, Visual validation, Chemoinformatics, QSAR/QSPR modelling, Information technology, T58.5-58.64, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1758-2946

URL الوصول: https://doaj.org/article/7d494abc59c643569706c1f19327fdc1

المؤلفون: Tal, Tamara, Myhre, Oddvar, Fritsche, Ellen, Rüegg, Joëlle, Craenen, Kai, Aiello-Holden, Kiara, Agrillo, Caroline, Babin, Patrick J., Escher, Beate I., Dirven, Hubert, Hellsten, Kati, Dolva, Kristine, Hessel, Ellen, Heusinkveld, Harm J., Hadzhiev, Yavor, Hurem, Selma, Jagiello, Karolina, Judzinska, Beata, Klüver, Nils, Knoll-Gellida, Anja, Kühne, Britta A., Leist, Marcel, Lislien, Malene, Lyche, Jan L., Müller, Ferenc, Colbourne, John K., Neuhaus, Winfried, Pallocca, Giorgia, Seeger, Bettina, Scharkin, Ilka, Scholz, Stefan, Spjuth, Ola, Professor, 1977, Torres-Ruiz, Monica, Bartmann, Kristina

المصدر: Frontiers in Toxicology. 6

مصطلحات موضوعية: new approach method (NAM), developmental neurotoxicity (DNT), adult neurotoxicity (ANT), DNT-IVB, zebrafish, applicability domain

وصف الملف: electronic

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-530600
https://doi.org/10.3389/ftox.2024.1359507
https://uu.diva-portal.org/smash/get/diva2:1869623/FULLTEXT01.pdf

المؤلفون: Efrén Pérez-Santín, Luis de-la-Fuente-Valentín, Mariano González García, Kharla Andreina Segovia Bravo, Fernando Carlos López Hernández, José Ignacio López Sánchez

المصدر: AIMS Mathematics, Vol 8, Iss 11, Pp 27858-27900 (2023)

مصطلحات موضوعية: applicability domain, oecd principles, quantitative structure-activity relationship (qsar), toxicity, machine learning, Mathematics, QA1-939

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2473-6988

URL الوصول: https://doaj.org/article/49f655e55aa24e5bbe94bd5d60bdf9b2

المؤلفون: Kajjana Boonpalit, Jiramet Kinchagawat, Dr. Supawadee Namuangruk

المصدر: ChemElectroChem, Vol 11, Iss 10, Pp n/a-n/a (2024)

مصطلحات موضوعية: Machine Learning, Applicability Domain, Active Learning, Self-supervised Learning, Material Discovery, Industrial electrochemistry, TP250-261, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2196-0216

URL الوصول: https://doaj.org/article/86aa92029d6e4f2d9ff6880e829662cd

المؤلفون: Srijit Seal, Hongbin Yang, Maria-Anna Trapotsi, Satvik Singh, Jordi Carreras-Puigvert, Ola Spjuth, Andreas Bender

المصدر: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-16 (2023)

مصطلحات موضوعية: Machine learning, Cell Painting, Structure, Toxicity, Bioactivity, Applicability domain, Information technology, T58.5-58.64, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1758-2946

URL الوصول: https://doaj.org/article/e1f6430fd7444ba5b221a6795ab8bd50

المؤلفون: Ozren Jovic, RABAH MOURAS

مصطلحات موضوعية: Chemical sciences, solubility, machine learning, extreme gradient boosting, variable selection, conformal predictor, prediction interval, applicability domain, molecular descriptor

Relation: https://figshare.com/articles/journal_contribution/Extreme_gradient_boosting_combined_with_conformal_predictors_for_informative_solubility_estimation/25011581

الاتاحة: https://doi.org/10.34961/researchrepository-ul.25011581.v1
https://figshare.com/articles/journal_contribution/Extreme_gradient_boosting_combined_with_conformal_predictors_for_informative_solubility_estimation/25011581

المؤلفون: Brandmair, K., Dising, Denise, Finkelmeier, Doris, Schepky, A., Kuehnl, J., Ebmeyer, J., Burger-Kentischer, Anke

مصطلحات موضوعية: 3D epidermis model, applicability domain, new approach methodology, Nrf2, reporter gene, Skin sensitization

Relation: Toxicology; https://publica.fraunhofer.de/handle/publica/464308

الاتاحة: https://publica.fraunhofer.de/handle/publica/464308
https://doi.org/10.1016/j.tox.2024.153743

المؤلفون: Yoshihiro Uesawa

المصدر: International Journal of Molecular Sciences, Vol 25, Iss 3, p 1373 (2024)

مصطلحات موضوعية: Ames test, quantitative structure–activity relationship, applicability domain, in silico study, machine learning, predictive performance, Biology (General), QH301-705.5, Chemistry, QD1-999

Relation: https://www.mdpi.com/1422-0067/25/3/1373; https://doaj.org/toc/1661-6596; https://doaj.org/toc/1422-0067; https://doaj.org/article/20f34a1d1402430fadd9ab085f3ad8d6

الاتاحة: https://doi.org/10.3390/ijms25031373
https://doaj.org/article/20f34a1d1402430fadd9ab085f3ad8d6

المؤلفون: 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

المصدر: Artificial Intelligence in the Life Sciences, Vol 3, Iss , Pp 100070- (2023)

مصطلحات موضوعية: Small molecule drug discovery, MELLODDY, QSAR, Federated multitask learning, Applicability domain, Uncertainty, Science (General), Q1-390

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2667318523000144; https://doaj.org/toc/2667-3185

URL الوصول: https://doaj.org/article/c22676311fae4ecf96bc8d92b6788cad

المؤلفون: Alvarsson, Jonathan, Arvidsson McShane, Staffan, Norinder, Ulf, 1956, Spjuth, Ola

المصدر: Journal of Pharmaceutical Sciences. 110(1):42-49

مصطلحات موضوعية: QSAR, applicability domain, confidence, conformal prediction, predictive modeling

وصف الملف: print

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-86813
https://doi.org/10.1016/j.xphs.2020.09.055

المؤلفون: Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin, Da Shi, Brian J. Bennion, Xiaohua Zhang, Felice C. Lightstone

المصدر: Artificial Intelligence Chemistry, Vol 1, Iss 1, Pp 100004- (2023)

مصطلحات موضوعية: Uncertainty quantification, Neural networks, Drug discovery, Applicability domain, Chemistry, QD1-999, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2949747723000040; https://doaj.org/toc/2949-7477

URL الوصول: https://doaj.org/article/efcc644112614a30945cbc2b09c71d37

المؤلفون: Rado, Omesaad A. M.

مصطلحات موضوعية: Machine learning, Classification algorithms, Binary classification, Accuracy, Model evaluation, Model reliability, Applicability domain, Model robustness, Model coverage, Healthcare data

URL الوصول: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.828395

المؤلفون: Cindy Trinh, Silvia Lasala, Olivier Herbinet, Dimitrios Meimaroglou

المصدر: Algorithms, Vol 16, Iss 12, p 573 (2023)

مصطلحات موضوعية: machine learning, QSPR/QSAR, high-dimensional data, descriptors, thermodynamic properties, applicability domain, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: https://www.mdpi.com/1999-4893/16/12/573; https://doaj.org/toc/1999-4893

URL الوصول: https://doaj.org/article/a214218bc4b1403d85876d1e645c33b6

المؤلفون: Yimeng Wang, Yaxin Gu, Chaofeng Lou, Yuning Gong, Zengrui Wu, Weihua Li, Yun Tang, Guixia Liu

المصدر: Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-15 (2022)

مصطلحات موضوعية: Selective JAK inhibitors, GNN, Multitask learning, Model interpretations, Key substructures, Applicability domain, Information technology, T58.5-58.64, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1758-2946

URL الوصول: https://doaj.org/article/5c5c5ca76b4d4798ad399ffcb1030d33

المؤلفون: Mervin, Lewis

المساهمون: Bender, Andreas, Engkvist, Ola

مصطلحات موضوعية: 615.1, Cheminformatics, Mode of action, In silico, Protein Target Prediction, Orthologue, Chemical space, AstraZeneca, Chemistry Connect, Bioactivity data, Target deconvolution, Target prediction, MoA, ChEMBL, PubChem, Functional prediction, Sphere exclusion, Random Forest, Naive Bayes, SVM, Support Vector Machine, AD-AUC, Activation, Inhibition, Functional Effects, Mechanism-of-action, Mode-of-action, Mechanism of action, Phenotypic screens, High throughput screens, High content screens, PR-AUC, Applicability domain, Venn Abers, Platt scaling, Isotonic regression scaling, Python, Scikit-learn, RDKit

URL الوصول: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763562

المؤلفون: Pérez-Santín, Efrén, de-la-Fuente-Valentín, Luis, González García, Marian, Segovia Bravo, Kharla Andreina, López Hernández, Fernando Carlos, López Sánchez, José Ignacio

مصطلحات موضوعية: applicability domain, machine learning, OECD principles, quantitative structure-activity relationship (QSAR), toxicity, Scopus, JCR

Relation: vol. 8, nº 11; Efrén Pérez-Santín, Luis de-la-Fuente-Valentín, Mariano González García, Kharla Andreina Segovia Bravo, Fernando Carlos López Hernández, José Ignacio López Sánchez. Applicability domains of neural networks for toxicity prediction[J]. AIMS Mathematics, 2023, 8(11): 27858-27900. doi:10.3934/math.20231426; https://reunir.unir.net/handle/123456789/15478; https://doi.org/10.3934/math.20231426

الاتاحة: https://reunir.unir.net/handle/123456789/15478
https://doi.org/10.3934/math.20231426

المؤلفون: Kumari, Priyanka, Van Laethem, Thomas, Hubert, Philippe, Fillet, Marianne, Sacre, Pierre-Yves, Hubert, Cédric

المساهمون: CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège

المصدر: Molecules, 28 (4), 1696 (2023-02-10)

مصطلحات موضوعية: QSRR, RPLC, applicability domain, machine learning, stacking, Reproducibility of Results, Chromatography, Liquid/methods, Algorithms, High Pressure Liquid/methods, Quantitative Structure-Activity Relationship, Reverse-Phase, High Pressure Liquid, Liquid, Analytical Chemistry, Pharmaceutical Science, Human health sciences, Pharmacy, pharmacology & toxicology, Sciences de la santé humaine, Pharmacie, pharmacologie & toxicologie

Relation: https://www.mdpi.com/1420-3049/28/4/1696/pdf; urn:issn:1420-3049; https://orbi.uliege.be/handle/2268/301951; info:hdl:2268/301951; info:pmid:36838689

الاتاحة: https://orbi.uliege.be/handle/2268/301951
https://orbi.uliege.be/bitstream/2268/301951/1/molecules-28-01696-with-cover.pdf
https://doi.org/10.3390/molecules28041696

المؤلفون: Alberto Danieli, Erika Colombo, Giuseppa Raitano, Anna Lombardo, Alessandra Roncaglioni, Alberto Manganaro, Alessio Sommovigo, Edoardo Carnesecchi, Jean-Lou C. M. Dorne, Emilio Benfenati

المصدر: International Journal of Molecular Sciences; Volume 24; Issue 12; Pages: 9894

مصطلحات موضوعية: in silico models, new approach methodologies (NAMs), toxicological endpoints, applicability domain (AD), VEGA tool

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Molecular Informatics; https://dx.doi.org/10.3390/ijms24129894

الاتاحة: https://doi.org/10.3390/ijms24129894

المؤلفون: М. И. Шаладонова, Я. В. Диченко, С. А. Усанов, M. I. Shaladonova, Ya. V. Dzichenka, S. A. Usanov

المصدر: Doklady of the National Academy of Sciences of Belarus; Том 67, № 5 (2023); 388-398 ; Доклады Национальной академии наук Беларуси; Том 67, № 5 (2023); 388-398 ; 2524-2431 ; 1561-8323 ; 10.29235/1561-8323-2023-67-5

مصطلحات موضوعية: идентификация препаратов, ингибиторы ароматазы, лиганд, топологические дескрипторы, машинное обучение, прогностическая модель, область применимости, aromatase inhibitors, ligand, topological descriptors, machinery learning, predictive model, applicability domain, drug identification

وصف الملف: application/pdf

Relation: https://doklady.belnauka.by/jour/article/view/1152/1151; Guha, R. Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors / R. Guha, P. C. Jurs // J. Chem. Inf. Comput. Sci. – 2004. – Vol. 44, N 6. – P. 2179–2189. doi:10.1021/ci049849f; Novel triazole-tetrahydroisoquinoline hybrids as human aromatase inhibitors / C. Chamduang [et al.] // Bioorg. Chem. – 2019. – Vol. 93. – Art. 103327. doi:10.1016/j.bioorg.2019.103327; Brueggemeier, R. W. Aromatase Inhibitors in the Treatment of Breast Cancer / R. W. Brueggemeier, J. C. Hackett, E. S. Diaz-Cruz // Endocrine Rev. – 2005. – Vol. 26, N 3. – P. 331–345. doi:10.1210/er.2004-0015; Bertelli, G. Sequencing of aromatase inhibitors / G. Bertelli // Br. J. Cancer. – 2005. – Vol. 93, N S1. – P. 6–9. doi:10.1038/sj.bjc.6602689; Studies on non-steroidal inhibitors of aromatase enzyme; 4-(aryl/heteroaryl)-2-(pyrimidin-2-yl) thiazole derivatives / Z. Sahin [et al.] // Bioorg. Med. Chem. – 2018. – Vol. 26, N 8. – P. 1986–1995. doi:10.1016/j.bmc.2018.02.048; Aromatase Inhibitors Evolution as Potential Class of Drugs in the Treatment of Postmenopausal Brest Cancer Women / S. Avvaru [et al.] // Mini-Rev. Med. Chem. – 2018. – Vol. 18, N 7. – P. 609–621. doi:10.2174/1389557517666171101100902; Determining the IC50 Values for Vorozole and Letrozole, on a Series of Human Liver Cytochrome P450s, to Help Determine the Binding Site of Vorozole in the Liver / L. Raymond [et al.] // Enzyme Research. – 2015. – Vol. 2015. – P. 1–4. doi:10.1155/2015/321820; Synthesis of Aromatase Inhibitors and Dual Aromatase Steroid Sulfatase Inhibitors by Linking an Arylsulfamate Motif to 4-(4H-1,2,4-triazol-4-ylamino)benzonitrile: SAR, Crystal Structures, in vitro and in vivo Activities / C. Bubert [et al.] // ChemMedChem. – 2008. – Vol. 3, N 11. – P. 1708–1730. doi:10.1002/cmdc.200800164; Баскин, И. И. Введение в хемоинформатику / И. И. Баскин, Т. И. Маджидов, А. А. Варнек. – М., Казань, Страсбург, 2020. – Ч. 4: Методы машинного обучения. – 321 с.; Application of the Random Forest Method in Studies of Local Lymph Node Assay Based Skin Sensitization Data / S. Li [et al.] // J. Chem. Inf. Model. – 2005. – Vol. 45, N 4. – P. 952–964. doi:10.1021/ci050049u; Применение метода количественных корреляций структура–свойство (ККСС) с использованием топологического индекса Балабана на примере группы сульфаниламидов / А. В. Сыроешкин [и др.] // Вестн. Рос. ун-та дружбы народов. Сер. Медицина. – 2000. – № 2. – С. 80–83.; Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images / D. Liu [et al.] // Arch. Gynecol. Obstet. – 2021. – Vol. 303, N 3. – Р. 811–820. doi:10.1007/s00404-020-05908-5; Janitza, S. An AUC-based permutation variable importance measure for random forests / S. Janitza, C. Strobl, A.-L. Boulesteix // BMC Bioinformatics. – 2013. – Vol. 14, N 1. – Р. 1–11. doi:10.1186/1471-2105-14-119; https://doklady.belnauka.by/jour/article/view/1152

الاتاحة: https://doklady.belnauka.by/jour/article/view/1152
https://doi.org/10.29235/1561-8323-2023-67-5-388-398