Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Dec 9, 2024
Date Accepted: Apr 23, 2025
Harnessing AI and Quantum Computing: Revolutionizing Drug Discovery and Approval Processes Using Collagen as an Example
ABSTRACT
Background:
Computational data generated from Digital Computers, Artificial Intelligence, and Quantum Computing will change the course of new drug discovery and approval by accelerating and optimizing the process of identifying potential drug candidates through creating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety.
Objective:
This study aims to identify if computational data from Digital Computers, Artificial Intelligence, and Quantum Computing in computer-aided drug development can reduce the number of laboratory and animal experiments by providing safe and effective combinations while minimizing the costs and time in drug development. This will suggest computational models based on Digital Computers, Artificial Intelligence, and Quantum Computing.
Methods:
Methods:
Overall, 83 academic publications were reviewed, pharmaceutical manufacturers were interviewed, and AI was utilized to run computational data. The research evidence mainly focused on the ability to create computational virtual data to be compared to actual laboratory data and the use of this data to discover or approve newly discovered drugs.
Results:
Digital computers, artificial intelligence, and quantum computing offer unique capabilities to tackle complex problems in drug discovery, which is a critical challenge in pharmaceutical research. Regulatory agents will need to adapt to these new technologies. Regulatory processes may become more streamlined, utilizing adaptive clinical trials, accelerating pathways, and better integrating digital data to reduce the time and cost of bringing new drugs to market.
Conclusions:
Computational data methods could be used to reduce the cost and time involved in experimental drug discovery, allowing researchers to simulate biological interactions and screen large compound libraries more efficiently. Creating virtual data for drug discovery involves several stages, each utilizing specific methods such as simulations, synthetic data generation, data augmentation, and tools to generate, collect, and affect human interaction to identify and develop new drugs. Clinical Trial: N/A
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Copyright
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