Revolutionizing Drug Discovery: The Role of AI and Machine Learning DOI
Abhinav Vashishat, Ghanshyam Das Gupta, Balak Das Kurmi

и другие.

Current Pharmaceutical Design, Год журнала: 2023, Номер 29(39), С. 3087 - 3088

Опубликована: Ноя. 1, 2023

Язык: Английский

Machine learning in toxicological sciences: opportunities for assessing drug toxicity DOI Creative Commons

Lusine Tonoyan,

Arno G. Siraki

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

11
Network pharmacology and molecular docking: combined computational approaches to explore the antihypertensive potential of Fabaceae species DOI Creative Commons

Zainab Shahzadi,

Zubaida Yousaf, İrfan Anjum

и другие.

Bioresources 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

8
The recent advances in the approach of artificial intelligence (AI) towards drug discovery DOI Creative Commons

Mahroza Kanwal Khan,

Mohsin Ali Raza, Muhammad Shahbaz

и другие.

Frontiers 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

7
Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs) DOI
Amr E. Abbas, Fei Ye

International Journal of Biological Macromolecules, Год журнала: 2024, Номер 277, С. 134293 - 134293

Опубликована: Июль 29, 2024

Язык: Английский

Процитировано

7
The dark side of beauty: an in-depth analysis of the health hazards and toxicological impact of synthetic cosmetics and personal care products DOI Creative Commons
Abdullah M. Alnuqaydan

Frontiers 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

7
A review of transformers in drug discovery and beyond DOI Creative Commons
Jian Jiang, Long Chen, Ke Lü

и другие.

Journal of Pharmaceutical Analysis, Год журнала: 2024, Номер unknown, С. 101081 - 101081

Опубликована: Авг. 1, 2024

Язык: Английский

Процитировано

7
Analysis of gene regulatory networks from gene expression using graph neural networks DOI

Hakan T. Otal,

Abdülhamit Subaşı,

Furkan Kurt

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 249 - 270

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity DOI
Konda Mani Saravanan,

Jiang-Fan Wan,

Liujiang Dai

и другие.

Methods, Год журнала: 2024, Номер 226, С. 164 - 175

Опубликована: Май 1, 2024

Язык: Английский

Процитировано

5
Introduction to the Special Issue: AI Meets Toxicology DOI
Günter Klambauer, Djork-Arné Clevert, Imran Shah

и другие.

Chemical 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

9
A Comparative Study of Deep Learning Models and Classification Algorithms for Chemical Compound Identification and Tox21 Prediction DOI
Yusuf Alaca, Berkay Emi̇n, Akif Akgül

и другие.

Computers & Chemical Engineering, Год журнала: 2024, Номер 189, С. 108805 - 108805

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

3