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Computational intelligence in drug development

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational intelligence in drug development/ edited by Carlton A. Taft, Sergio Ricardo de Lazaro.
其他作者:	Taft, Carlton A.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	vi, 168 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Drug development - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-032-07366-2
ISBN:	9783032073662

Computational intelligence in drug development
Computational intelligence in drug development[electronic resource] /edited by Carlton A. Taft, Sergio Ricardo de Lazaro. - Cham :Springer Nature Switzerland :2025. - vi, 168 p. :ill., digital ;24 cm. - Series in bioengineering,2196-887X. - Series in bioengineering..

1. Molecular dynamics and prediction of oral toxicity in Alzheimer's disease -- 2. In silico prediction of ADMET parameters, mechanisms of elaboration and performance of computational tools -- 3. Predicting Aloe Vera herb drug interactions -- 4. Enzymes as targets for infectious disease control -- 5. A computational analysis of new strategies against type 2 diabetes mellitus.

This book offers a comprehensive overview of cutting-edge computational strategies in drug discovery, with a focus on in silico approaches that accelerate the identification and optimization of therapeutic candidates. Readers will find detailed insights into molecular docking, molecular dynamics simulations, and structure-based drug design, alongside predictive models for toxicity, pharmacokinetics (ADMET), and enzyme inhibition. By combining theoretical foundations with practical applications, the volume highlights how artificial intelligence and computational chemistry are reshaping modern pharmaceutical research and enabling the development of safer, more effective drugs.

ISBN: 9783032073662

Standard No.: 10.1007/978-3-032-07366-2doiSubjects--Topical Terms:

964848
Drug development
--Data processing.

LC Class. No.: RM301.25

Dewey Class. No.: 615.190028563

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520 $a This book offers a comprehensive overview of cutting-edge computational strategies in drug discovery, with a focus on in silico approaches that accelerate the identification and optimization of therapeutic candidates. Readers will find detailed insights into molecular docking, molecular dynamics simulations, and structure-based drug design, alongside predictive models for toxicity, pharmacokinetics (ADMET), and enzyme inhibition. By combining theoretical foundations with practical applications, the volume highlights how artificial intelligence and computational chemistry are reshaping modern pharmaceutical research and enabling the development of safer, more effective drugs.
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