Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Yogesh Chaudhari; Manisha Chaudhari; Abhijeet Puri; Mahesh Nemade

doi:10.20944/preprints202404.1013.v3

Preprint Review Version 3 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 11 April 2024 / Approved: 15 April 2024 / Online: 16 April 2024 (10:32:47 CEST)
Version 2 : Received: 17 April 2024 / Approved: 18 April 2024 / Online: 19 April 2024 (08:01:11 CEST)
Version 3 : Received: 20 April 2024 / Approved: 22 April 2024 / Online: 22 April 2024 (18:38:54 CEST)

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

computational pharmaceutics; AI; multi-scale modeling; quantum computing; drug discovery; blockchain frameworks

Medicine and Pharmacology, Pharmacy

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Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

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