Artificial intelligence has the potential to identify the right pharmaceutical component in drug development

The pharmaceutical industry is a slow learner when it comes to implying digital health technology. Pharma companies have so far delayed the idea of using artificial intelligence and machine learning strategies to develop drugs. Artificial intelligence has the potential to make extraordinary innovation wave in drug discovery. However, the pharmaceutical sector should work on filling the gap between understanding these possibilities and applying them to the drug discovery and development process.

Healthcare industry has rapidly embraced artificial intelligence into the working system. AI and its sub-technologies are helping the medical industry on a large scale. However, the pharmaceutical industry is still on the initial stage of leveraging digital technologies to accelerate the drug development process. The main goal of drug discovery is to identify the medicine that acts beneficially on the body. Finding the right drug involves a lengthy process of carrying out large screen libraries of molecules that can specifically bind to a target molecule involved in a disease. The mission to find the right drug goes through numerous rounds of tests to develop it into a promising compound. According to Taconic Biosciences's tally, an incredible amount of time and money goes into drug development and bringing a drug to market costs about US$2.8 billion over 12+ years. Fortunately, artificial intelligence can help pharmaceutical industry to find the right drug and develop it. Artificial intelligence uses personified knowledge and learns from solutions it produces to address not only specific but also complex problems in medicine.

AI platform is used for drug development

Drug development is a long process if conducted manually. Initially, researchers have to identify the target protein that is causing the disease and study it for a long time. Next, they try to find which component or a molecule would influence the protein. During this process, researchers make sure that inefficient components are kept aside and only safe, efficient components are taken further. The role of AI in drug discovery starts with finding the molecule that better address the protein. Researchers can't test the hundreds and thousands of molecules in market. It is both lengthy and expensive. Fortunately, AI platforms replace the long testing process with a simple analysis. Researchers feed in parameters into the AI platforms and make them run an analysis on the molecules. AI platform identifies the right component that can be used for drug development.

Applying neural network in drug discovery

Even though deep neural network has been around the tech radar for decades, it got a wide range of attention only in 2012. Researchers from the University of Toronto won the ImageNet Large Scale Visual Recognition Challenge (ILSVR) are using deep neural network. Currently, pharmaceutical companies are using various types of deep neural networks to explore classical statistical techniques. The technology helps in finding the right molecule that is responsible for certain activities. Deep neural network gives an immediate indication to chemists of what to do in order to remove certain unwanted activities. This deep neural network model is also used by chemists to judge their compound ideas before deciding on whether to synthesize them or not

Big data fed into AI helps drug development

Healthcare data is huge and critical. Today, millions of research, feedback, reports, patient records and a whole lot of other things related to the healthcare industry are fed into AI in form of big data. Even though healthcare sector is pretty slow in availing solutions from them, medical institutions are trying their best to stay ahead in the race. Artificial intelligence systems are featured with an apt mechanism to go through data and make meaningful interpretations out of that. Deep learning programs run on the data and learn more about the proteins whose presence makes a difference between healthy patients and an ill one. Meanwhile, machine learning abilities strive to find and establish connections between proteins and diseases.

AI in Phase wise drug discovery

Before the Covid-19 pandemic outbreak, no one thought that a vaccine process could be fast-tracked so much. Generally, making a vaccine and testing it on a trial basis involves years of research and observation. However, the pandemic has broken the routine. Governments across the globe were running a race to come up with an effective vaccine as soon as possible. The funding into pharmaceutical industry also skyrocketed during the period. With accelerating the trials and emergency approvals on the bag, pharmaceutical companies leveraged AI to complement the vaccine making process.

AI in drug discovery (Phase 1): Discovering the right drug involves reading and analysing already existing literature and testing the ways potential drugs interact with targets. AI performs the tasks faster than humans and provides rapid results.

AI in preclinical development (Phase 2): During the preclinical development phase, the drug is tested on animals to see how they perform. Unveiling AI in this phase will help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model.

AI in clinical trials (Phase 3): Researchers begin testing the drug on human bodies during the clinical trial. AI can facilitate participant monitoring during clinical trials, generating a larger set of data more quickly and aid in participant retention by personalizing the trial experience.

The ethical drawback

Even though AI is helping drug discovery to a large range, it also raises some remarkable ethical questions. Patient data are hectic in healthcare industry. If these critical data gets to the hands of hackers, there are chances that it'll be used for evil purposes. Henceforth, patient privacy needs to be maintained. Unlike many other sectors, there are no regulations or policies that direct drug makers to go on a drawn line. It is up to the pharmaceutical companies to secure patient data and use it in the right way.