3 AI trends in drug discovery that stood out in 2022

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There is no doubt that 2022 has seen a wave of AI innovation and use cases for businesses across many industries. AI has expanded beyond marketing, customer satisfaction and employee retention. One of the areas where she has made major breakthroughs is in medicine, biotechnology and pharmacology, where she is transforming drug discovery and development.

The cost of drug discovery and development averages $1.3 billion and “it takes 12 to 15 years to get to market,” according to a PubMed article. It is therefore not surprising that the drug discovery industry has seen a significant increase in AI-based technologies. An example of this is an article in Nature which notes that the integration of AI into the drug discovery and development pipeline has increased by almost 40% per year.

According to healthcare investors Tzvi Bessler and Morris Laster, Ph.D., “drug discovery companies are leveraging AI in a variety of ways, such as the use of learning algorithms to identify potential drug candidates, predict their efficacy and safety, and optimize their design For example, they use AI to analyze large data sets of biological and chemical information to identify patterns and relationships that may be relevant to drug discovery."

This, they said, helps companies “identify promising leads and accelerate the drug discovery process.”

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As the year of AI draws to a close, VentureBeat spoke with several experts about the most compelling 2022 AI trends in drug discovery. Here are three trends that stood out:

1. More efficiency in biological modeling and drug target discovery

James Handler, a professor at Rensselaer Polytechnic Institute and chairman of the Association for Computing Machinery Technology Policy Council, spoke to VentureBeat about two uses where AI is showing great promise in drug discovery: reducing the number of potential candidates for trials and provide potential explanations for secondary drug use, i.e. why a drug is effective for a condition it was not originally designed for to treat.

In either case, he noted, the key is that “AI can reduce the number of possibilities that need to be explored by traditional means.” This facilitates biological modeling and drug target discovery. “However,” he added, “an important aspect of this is that AI systems are able to explain their predictions to humans, a goal of current research. This allows humans to make the final decisions [on] analysis and testing, with AI dramatically reducing the cost of bringing effective drugs to market.”

Drug discovery and development typically begins with the identification of a biological target: a gene, protein, receptor, or enzyme, for example. Proteins are the most