AI in Drug Discovery Market Booms with the Rise in Data Digitalization in Pharmaceutical Sector
What is Artificial Intelligence?
Artificial intelligence refers to replicating human intelligence processes using machines, particularly computer systems. Certain applications of AI are natural language processing, expert systems, machine vision, and speech recognition.
Given the accelerated hype and interest in AI, companies are working on promoting how their services and products use AI. Often, when referring to AI, many think of just one component, machine learning. However, the technology requires a base of specialized software and hardware for training and writing machine learning algorithms.
AI generally works by ingesting huge volumes of labeled training data, assessing the data for patterns and correlations, and utilizing these patterns to predict future states. Like this, a chatbot being fed a series of text chats can learn to generate lifelike exchanges with consumers. Similarly, an image recognition tool can learn to detect and describe different objects in images by assessing multiple examples.
In the past decade, there has been a dramatic rise in data digitalization within the pharmaceutical industry. But this digitalization brings challenges in scrutinizing, acquiring, and using that knowledge to deal with complex clinical issues. This fosters the use of AI, as it can deal with huge amounts of data with high automation. AI is an advanced technology-based system that involves numerous advanced tools and networks that mimic/replicate human intelligence. Simultaneously, it can’t replace human physical presence entirely. AI uses software and systems that interpret and assess the input data to reach decisions and accomplish certain objectives. Its applications in the pharmaceutical sector are continuously extending.
The vast chemical space covering more than 1060 molecules accelerates the development of a massive number of drug molecules. But the lack of novel technologies curbs the drug development process, turning it into a time-consuming, expensive, and complex task that can be handled with AI. For example, AI can detect hit and lead compounds while offering a faster validation concerning the drug target and optimizing the drug structure design.
Numerous parameters, including the similarity of molecules, predictive models, the application of in silico approaches, and the molecule generation process, are utilized to predict the required chemical structure of a compound. For example, Pereira et al. recently introduced a novel system called DeepVS, which docks 40 receptors and 2950 ligands, showing impressive performance when close to 95 000 decoys were reviewed against these receptors.
Another approach involves the application of an automated replacement algorithm that optimizes a cyclin-dependent kinase-2 inhibitor’s potency profile by reviewing its biochemical activity, physicochemical properties, and shape similarity.
AI in Drug Discovery Market Status Quo
The heightened demand for the development and discovery of novel drug therapies, along with the surge in the production capacities of the life science sector, has been favorable for artificial intelligence in the drug discovery market. However, manufacturers active in the life science sector are mostly focused on replenishing their product pipelines since most of the leading sellers are going off patent.
Furthermore, many private-public partnerships are fostering the use of AI-powered systems and n solutions in drug discovery, which will benefit the worldwide market. Countries like the U.S., France, Japan, and Spain are at the forefront of the clinical trial field, while the United Kingdom is mostly focused on improving research & development activities.