Artificial Intelligence plays a vital role in drug discovery, from target selection to clinical trials with minimum-labor and maximum efficiency.
Artificial intelligence has been an efficient contributor to drug development during the Covid-19 pandemic. AI was used to identify and replicate protein structure. It also assisted in accelerating drug discovery through validation and virtual screening. During the pandemic, scientists from TCS Innovation Lab had announced their drug discovery process using AI. The Hindu covered an article on the same which says, “Drug discovery is a complex process, needing several layers of validation before the drug may come in use. In this work, the researchers have brought down the time taken for the initial step of designing suitable candidate molecules for testing from years to just a week, reinforcing the power of AI in handling huge datasets.”
The traditional development processes are time-consuming and involve a huge cost. AI and its subsets like machine learning make it easier to design a drug. The pharmaceutical industry has been a slow adopter of technologies and the shift towards artificial intelligence is a huge step for the industry. Although, the benefits make it easily acceptable. Let us look at some of the applications of AI in the process of drug development.
• Validation And Molecular Target Identification
Molecular target identification is one of the initial stages in drug development. This involves identifying genes, proteins, or other molecules that can be used as a target to measure drug potency. AI and machine learning are used to analyze molecules, predict, and prioritize effective targets. These targets are usually identified based on their druggability and safety. AI uses computer vision to understand 3D protein structures and molecules.
• Drug Repurposing
Drugs often interact with a lot of other molecules than the target molecule. Hence, it is imperative to identify the drug-molecule interaction to repurpose drugs. This way a drug can be used to treat other diseases with no effective cure, and also to find new molecular targets. Machine learning and deep learning helps analyze the interaction between the drug and off-target molecules to establish any possible relation. This way, AI along with network medicine technologies can be used to reduce the gap between drug development and its implementation in clinical practices.
• Clinical Trials
This part of the process of drug development is probably the slowest one. It takes a lot of time to manually analyze and monitor data. Thus, it is also at the peril of having human errors which will eventually lead to the failure of drug trials. AI algorithms possess the ability to process humongous chunks of data and thus bring quicker and accurate results. AI in clinical trials will yield better patient compliance and reduce the cost of trials. It will improve the efficiency of identifying the impact of drugs and treatment with reliable evidence.
What Does The Future Look Like?
According to a research report by Acumen, the Global Artificial Intelligence for Drug Discovery Market size is expected to reach around 8,149 million US dollars by 2026. The booming healthcare sector, an increase in patients, and the discovery of new diseases each day have brought the pharma industry to the limelight. The pharmaceutical industry is in high demand for the development of different drugs and medicines. They are moving towards innovation by adopting AI-driven technologies. These technologies enable cost-effective drug development with minimum-labor and maximum ROI. Artificial intelligence is set to revolutionize drug design and development.
