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SAR and QSAR in Environmental Research Volume 24, 2013 - Issue 3
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Article

Comparative study to predict toxic modes of action of phenols from molecular structures

Y. Brito-Sánchez Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba; Toxicology Center, Medical Sciences University, Santa Clara, Cuba
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J.A. Castillo-Garit Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba; Applied Chemistry Research Center, Faculty of Chemistry-Pharmacy, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba; Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, València, SpainCorrespondence[email protected] [email protected] [email protected]
,
H. Le-Thi-Thu Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba
,
Y. González-Madariaga Toxicology Center, Medical Sciences University, Santa Clara, Cuba
,
F. Torrens Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, València, Spain
,
Y. Marrero-Ponce Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba; Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, València, Spain
&
J.E. Rodríguez-Borges Centro Investigação em Química (UP), Departamento de Química, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
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Pages 235-251 | Received 02 Nov 2012, Accepted 10 Jan 2013, Published online: 25 Feb 2013

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R. Zhang, Y. Chen, D. Fan, T. Liu, Z. Ma, Y. Dai, Y. Wang & Z. Zhu. (2023) Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model. SAR and QSAR in Environmental Research 34:10, pages 789-803.
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Y. Cañizares-Carmenate, N.-H. Nam, R. Díaz-Amador, N.T. Thuan, P.T.P. Dung, F. Torrens, H. Pham-The, F. Perez-Gimenez & J.A. Castillo-Garit. (2022) Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer’s disease treatment. SAR and QSAR in Environmental Research 33:1, pages 49-61.
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R. Sheikhpour, M. A. Sarram, M. Rezaeian & E. Sheikhpour. (2018) QSAR modelling using combined simple competitive learning networks and RBF neural networks. SAR and QSAR in Environmental Research 29:4, pages 257-276.
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J. A. Castillo-Garit, G. M. Casañola-Martin, S. J. Barigye, H. Pham-The, F. Torrens & A. Torreblanca. (2017) Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis. SAR and QSAR in Environmental Research 28:9, pages 735-747.
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J. Devillers, C. Lagneau, A. Lattes, J.C. Garrigues, M.M. Clémenté & A. Yébakima. (2014) In silico models for predicting vector control chemicals targeting Aedes aegypti. SAR and QSAR in Environmental Research 25:10, pages 805-835.
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Articles from other publishers (11)

Algafari Bakti Manggara, Kazufumi Ohkawa & Manabu Sugimoto. (2021) Classifying Modes of Toxic Action of Molecules with Electronic-structure Informatics. Application to Imbalanced Toxicity Data of Phenol Derivatives to Tetrahymena pyriformis . Chemistry Letters 50:11, pages 1887-1891.
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Alya A Arabi. (2021) Artificial intelligence in drug design: algorithms, applications, challenges and ethics. Future Drug Discovery 3:2.
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Svetoslav Slavov & Richard D. Beger. (2020) Quantitative structure–toxicity relationships in translational toxicology. Current Opinion in Toxicology 23-24, pages 46-49.
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Yudith Cañizares-Carmenate, Karel Mena-Ulecia, Yunier Perera-Sardiña, Francisco Torrens & Juan A. Castillo-Garit. (2019) An approach to identify new antihypertensive agents using Thermolysin as model: In silico study based on QSARINS and docking. Arabian Journal of Chemistry 12:8, pages 4861-4877.
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Alejandro Speck-Planche. (2019) Multicellular Target QSAR Model for Simultaneous Prediction and Design of Anti-Pancreatic Cancer Agents. ACS Omega 4:2, pages 3122-3132.
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Yuanyuan Ruan, Yingfeng Dou, Jingyi Chen, Alan Warren, Jiqiu Li & Xiaofeng Lin. (2018) Evaluation of phenol-induced ecotoxicity in two model ciliate species: Population growth dynamics and antioxidant enzyme activity. Ecotoxicology and Environmental Safety 166, pages 176-185.
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Yoan Martínez-López, Stephen J. Barigye, Oscar Martínez-Santiago, Yovani Marrero-Ponce, James Green & Juan A. Castillo-Garit. (2017) Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors. Environmental Toxicology and Pharmacology 56, pages 314-321.
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Gerardo M. Casa?ola-Martin, Huong Le-Thi-Thu, Facundo P?rez-Gim?nez, Yovani Marrero-Ponce, Matilde Merino-Sanju?n, Concepci?n Abad & Humberto Gonz?lez-D?az. (2015) Multi-output model with Box?Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin?proteasome pathway. Molecular Diversity 19:2, pages 347-356.
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Yoan Brito‐Sánchez, Yovani Marrero‐Ponce, Stephen J. Barigye, Iván Yaber‐Goenaga, Carlos Morell Pérez, Huong Le‐Thi‐Thu & Artem Cherkasov. (2015) Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set. Molecular Informatics 34:5, pages 308-330.
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Nilanjana Jain (Pancholi), Swagata Gupta, Neelima Sapre & Nitin S. Sapre. (2015) In silico de novo design of novel NNRTIs: a bio-molecular modelling approach. RSC Adv. 5:19, pages 14814-14827.
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Nilanjana Jain Pancholi, Swagata Gupta, Neelima Sapre & Nitin S. Sapre. (2014) Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1. Mol. BioSyst. 10:2, pages 313-325.
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