Current Pharmaceutical Design, Год журнала: 2023, Номер 29(39), С. 3087 - 3088
Опубликована: Ноя. 1, 2023
Язык: Английский
Current Pharmaceutical Design, Год журнала: 2023, Номер 29(39), С. 3087 - 3088
Опубликована: Ноя. 1, 2023
Язык: Английский
Frontiers in Drug Discovery, Год журнала: 2024, Номер 4
Опубликована: Фев. 8, 2024
Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML this field. This review discusses supervised, unsupervised, reinforcement their applications to toxicology. The application of the scientific method central development a model. These steps involve defining problem, constructing dataset, transforming data feature selection, choosing training model, validation, prediction. need rigorous models becoming more requirement due vast number chemicals interaction with biota. Large datasets make task possible, though selecting databases overlapping chemical spaces, amongst other things, an consideration. Predicting toxicity through machine can have significant societal impacts, including enhancements assessing risks, determining clinical toxicities, evaluating carcinogenic properties, detecting harmful side effects medications. We provide concise overview current state topic, focusing on potential benefits challenges related availability extensive datasets, methodologies analyzing these ethical implications involved applying such models.
Язык: Английский
Процитировано
11Bioresources and Bioprocessing, Год журнала: 2024, Номер 11(1)
Опубликована: Май 20, 2024
Abstract Hypertension is a major global public health issue, affecting quarter of adults worldwide. Numerous synthetic drugs are available for treating hypertension; however, they often come with higher risk side effects and long-term therapy. Modern formulations active phytoconstituents gaining popularity, addressing some these issues. This study aims to discover novel antihypertensive compounds in Cassia fistula , Senna alexandrina occidentalis from family Fabaceae understand their interaction mechanism hypertension targeted genes, using network pharmacology molecular docking. Total 414 were identified; initial screening was conducted based on pharmacokinetic ADMET properties, particular emphasis adherence Lipinski's rules. 6 compounds, namely Germichrysone, Benzeneacetic acid, Flavan-3-ol, 5,7,3',4'-Tetrahydroxy-6, 8-dimethoxyflavon, Dihydrokaempferol, Epiafzelechin, identified as effective agents. Most the found non-toxic against various indicators greater bioactivity score. 161 common targets obtained followed by compound-target construction protein–protein interaction, which showed role diverse biological system. Top hub genes TLR4, MMP9, MAPK14, AKT1, VEGFA HSP90AA1 respective associates. Higher binding affinities three Flavan-3-ol −7.1, −9.0 −8.0 kcal/mol, respectively. The MD simulation results validate structural flexibility two complexes Flavan-MMP9 Germich-TLR4 no. hydrogen bonds, root mean square deviations energies. concluded that C. (Dihydrokaempferol, Flavan-3-ol) (Germichrysone) have potential therapeutic constituents treat future drug formulation. Graphical
Язык: Английский
Процитировано
8Frontiers in Chemistry, Год журнала: 2024, Номер 12
Опубликована: Май 31, 2024
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium showing potential unprecedented advancements truth and efficiency. intersection to revolutionize drug discovery. However, also limitations experts should be aware these data access ethical issues. use techniques for discovery applications increased considerably over past few years, including combinatorial QSAR QSPR, virtual screening,
Язык: Английский
Процитировано
7International Journal of Biological Macromolecules, Год журнала: 2024, Номер 277, С. 134293 - 134293
Опубликована: Июль 29, 2024
Язык: Английский
Процитировано
7Frontiers in Public Health, Год журнала: 2024, Номер 12
Опубликована: Авг. 26, 2024
Over the past three decades, popularity of cosmetic and personal care products has skyrocketed, largely driven by social media influence propagation unrealistic beauty standards, especially among younger demographics. These products, promising enhanced appearance self-esteem, have become integral to contemporary society. However, users synthetic, chemical-based cosmetics are exposed significantly higher risks than those opting for natural alternatives. The use synthetic been associated with a variety chronic diseases, including cancer, respiratory conditions, neurological disorders, endocrine disruption. This review explores toxicological impact on human health, highlighting dangers posed various chemicals, rise ingredients, intricate effects chemical mixtures, advent nanotechnology in cosmetics, urgent need robust regulatory measures ensure safety. paper emphasizes necessity thorough safety assessments, ethical ingredient sourcing, consumer education, collaboration between governments, bodies, manufacturers, consumers. As we delve into latest discoveries emerging trends product regulation safety, it is clear that protection public health well-being critical concern this ever-evolving field.
Язык: Английский
Процитировано
7Journal of Pharmaceutical Analysis, Год журнала: 2024, Номер unknown, С. 101081 - 101081
Опубликована: Авг. 1, 2024
Язык: Английский
Процитировано
7Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 249 - 270
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1Methods, Год журнала: 2024, Номер 226, С. 164 - 175
Опубликована: Май 1, 2024
Язык: Английский
Процитировано
5Chemical Research in Toxicology, Год журнала: 2023, Номер 36(8), С. 1163 - 1167
Опубликована: Авг. 21, 2023
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTIntroduction to the Special Issue: AI Meets ToxicologyGünter KlambauerGünter KlambauerELLIS Unit Linz, LIT Lab & Institute for Machine Learning, Johannes Kepler University Altenbergerstraße 69, Linz 4040, AustriaMore by Günter Klambauerhttps://orcid.org/0000-0003-2861-5552, Djork-Arné ClevertDjork-Arné ClevertMachine Learning Research, Pfizer Worldwide Research Development and Medical, Linkstr. 10, Berlin 10785, GermanyMore Clevert, Imran ShahImran ShahCenter Computational Toxicology Exposure, Office of Development, U.S. Environmental Protection Agency, Triangle Park, North Carolina 27711, United StatesMore Shahhttps://orcid.org/0000-0003-0808-0140, Emilio BenfenatiEmilio BenfenatiDepartment Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano 20156, ItalyMore Benfenatihttps://orcid.org/0000-0002-3976-5989, Igor V. Tetko*Igor TetkoInstitute Structural Biology, Molecular Targets Therapeutics Center, Helmholtz Munich - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, GermanyBIGCHEM GmbH, Valerystr. 49, 85716 Unterschleißheim, Germany*Email: [email protected]More Tetkohttps://orcid.org/0000-0002-6855-0012Cite this: Chem. Res. Toxicol. 2023, 36, 8, 1163–1167Publication Date (Web):August 21, 2023Publication History Received21 July 2023Published online21 August inissue 21 2023https://doi.org/10.1021/acs.chemrestox.3c00217Copyright © Published 2023 American Chemical SocietyRequest reuse permissions This publication is free access through this site. Learn MoreArticle Views674Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InReddit (2 MB) Get e-AlertscloseSUBJECTS:Bioinformatics computational biology,Machine learning,Molecular modeling,Toxicity,Toxicology e-Alerts
Язык: Английский
Процитировано
9Computers & Chemical Engineering, Год журнала: 2024, Номер 189, С. 108805 - 108805
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
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