When it comes to incorporating digital health technologies, the pharmaceutical industry is a late learner. Pharmaceutical companies have delayed the concept of using AI and machine learning strategies to develop drugs. Artificial intelligence has the opportunity to generate an excellent wave of creativity in drug discovery. However, the pharmaceutical industry should work to fill the gap between recognizing these opportunities in drug discovery and production.
AI has been quickly incorporated into the working system by the healthcare industry. AI and its sub-technologies support the medical industry on a wide scale. However, the pharmaceutical industry is still at the initial stage of using emerging technology to speed up drug production. The primary objective of drug development is to define the drug that works efficiently on the body.
Identifying the right drug involves a lengthy process of carrying out large screen libraries of molecules. The journey to find the right drug goes through various tests to turn it into a promising compound. Luckily, AI will allow the pharmaceutical industry to find and grow the right medicine. AI uses personified knowledge and learns from solutions it produces to address specific and complex problems in medicine.
AI Platform is utilized for Drug Development
When performed manually, drug development is a long process. Initially, the target protein causing the disease must be identified by researchers and examined for a long time. Next, they try to decide which factor or a molecule will affect the protein. During this method, researchers ensure that inefficient components are held aside, and only healthy, successful components are taken further away.
The role of AI in drug discovery begins with finding the molecule that better direct the protein. The hundreds and thousands of molecules on the market can not be checked by researchers. It is both lengthy and expensive. Luckily, AI systems replace the lengthy testing process with a quick check. Researchers feed the AI platforms into parameters and make them perform an analysis on the molecules. The AI platform specifies the correct component that can be used for drug development.
Big Data fed into AI aids Drug Development
Data on healthcare is massive and vital. Today, millions of studies, patient records relevant to the healthcare sector are fed into AI in the form of big data. Although the healthcare industry is very reluctant to use its solutions, medical institutions do their best to stay ahead of the race. AI systems provide an appropriate framework for going through the information and creating concrete interpretations of it. Deep learning programs operate on the data and understand more about the proteins that distinguish between healthy and ill patients in their presence. Meanwhile, machine learning skills aim to find and create links between proteins and diseases.
AI in Phase wise Drug Discovery
No one thought before the COVID-19 pandemic outbreak that so much could be quick-tracked by a vaccine phase. Generally, it requires years of study and observation to produce a vaccine and test it on a trial basis. The pandemic, however, has disrupted the routine. Governments around the world have been running a race to find an efficient vaccine as soon as possible. Throughout the time, funding for the pharmaceutical industry also jumped. Pharmaceutical companies leveraged AI to supplement the vaccine making process by speeding the trials and emergency approvals on the bag.
- AI in Drug Discovery (Phase 1): Reading and reviewing existing literature and checking how new drugs interact with targets are involved in discovering the right drug. AI executes the tasks more rapidly than humans and offers fast performance.
- AI in Preclinical Development (Phase 2): The drug is tested on animals during the preclinical development phase to see if they work. In this step, unveiling AI will help trials run smoother and allow researchers to predict more quickly and effectively. Researchers will come to know if a drug will interact with the animal model.
- AI in Clinical Trials (Phase 3): During the clinical trial, researchers will start testing the drug on human bodies. AI will facilitate participant monitoring, more effectively producing a more significant set of data. By personalizing the experience of the trial, AI will aid in participant retention.
The Ethical Drawback
While AI helps to discover a wide variety of drugs, it also poses some tremendous ethical questions. Data on patients in the healthcare industry is hectic. If such sensitive information falls into hackers’ hands, it is likely to be used for evil purposes. Patient privacy must henceforth be maintained. There are no laws or policies that direct drug makers to go on a drawn line, unlike many other industries. It is up to the pharmaceutical industry to secure and use patient information in the correct way.
