Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity

Artificial intelligence (AI) and quantum computing will change the course of new drug discovery and approval. By generating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety, AI and quantum computing can accelerate and optimize the process of identifying potential drug candidates. In this viewpoint, we demonstrate how computational models obtained from digital computers, AI, and quantum computing can reduce the number of laboratory and animal experiments; thus, computer-aided drug development can help to provide safe and effective combinations while minimizing the costs and time in drug development. To support this argument, 83 academic publications were reviewed, pharmaceutical manufacturers were interviewed, and AI was used to run computational data for determining the toxicity of collagen as a case example. The research evidence to date has mainly focused on the ability to create computational in silico data for comparison to actual laboratory data and the use of these data to discover or approve newly discovered drugs. In this context, "in silico" describes scientific studies performed using computer algorithms, simulations, or digital models to analyze biological, chemical, or physical processes without the need for laboratory (in vitro) or live (in vivo) experiments. Digital computers, AI, 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, using adaptive clinical trials, accelerating pathways, and better integrating digital data to reduce the time and cost of bringing new drugs to market. 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 in silico data for drug discovery involves several stages, each using 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.