INTEGRATION OF ARTIFICIAL INTELLIGENCE AND NATURAL COMPOUNDS IN DRUG DEVELOPMENT

This thesis explores the integration of artificial intelligence (AI) and natural compounds in the development of modern pharmaceutical agents. AI-based molecular modeling and bioinformatics approaches can accelerate the identification of biologically active molecules by 25–30%, reducing both cost and time. Natural compounds such as Curcuma longa and Glycyrrhiza glabra show high pharmacological potential. The study emphasizes that combining computational prediction with experimental validation creates an innovative and sustainable approach to developing safe and effective medicines.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-08
DOI
https://doi.org/10.5281/zenodo.17853243
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

INTEGRATION OF ARTIFICIAL INTELLIGENCE AND NATURAL COMPOUNDS IN DRUG DEVELOPMENT

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

INTEGRATION OF ARTIFICIAL INTELLIGENCE AND NATURAL COMPOUNDS IN DRUG DEVELOPMENT

article en

Abstract

This thesis explores the integration of artificial intelligence (AI) and natural compounds in the development of modern pharmaceutical agents. AI-based molecular modeling and bioinformatics approaches can accelerate the identification of biologically active molecules by 25–30%, reducing both cost and time. Natural compounds such as Curcuma longa and Glycyrrhiza glabra show high pharmacological potential. The study emphasizes that combining computational prediction with experimental validation creates an innovative and sustainable approach to developing safe and effective medicines.

Zenodo (CERN European Organization for Nuclear Research)
Tashkent Pediatric Medical Institute (UZ)
Industry, innovation and infrastructure
Openalex Percentile: Top 33%
Computational Drug Discovery Methods
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