The Role of AI in Drug Discovery & Pharmaceutical Development

In order to fully appreciate AI’s impact on drug discovery, it is important to first define and understand artificial intelligence within the context of pharmaceutical development.

AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves using algorithms and statistical models to process data, identify patterns, and make predictions.

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Learn more about training AI algorithms in our article: “Exploring Generative AI: The Future of Creative and Analytical Technologies”

Defining Artificial Intelligence in the Context of Drug Discovery

When it comes to drug discovery, AI is utilized to expedite the identification of potential drug candidates. Traditionally, this process involved screening thousands or even millions of compounds to identify those with the desired therapeutic properties. With artificial intelligence, this process becomes much shorter. For example, the German biotechnology company Evotec, which uses an AI drug discovery platform, “Centaur Chemist” by Oxford-based Exscientia, managed to cut the drug discovery project from 5 years to 8 months.

This paves the road for the further use of AI in drug discovery. But artificial inteligence already has a long history of use in the pharmaceutical industry.

The Evolution of AI in the Pharmaceutical Industry

Over the years, AI has evolved from being a concept in science fiction to a practical tool.

Initially, it was primarily used for data analysis and mining. Now, AI is capable of far more complex tasks, such as virtual screening, compound optimization, and even predicting the probability of success for a particular drug candidate. Or more. For example, medical companies Roche, Genentech and AstraZeneca are using an AI platform called Trial Pathfinder. The platform automates selecting patients eligible for clinical trials (based on their diagnoses, blood test result, age, weight, etc.). Since the results provided by Pathfinder are accurate, the solution helped to double the number of patients participating in experiments without increasing the hazard ratio.

One of the key advantages of AI in pharmaceutical development is its ability to analyze and interpret vast amounts of data. In drug discovery, researchers generate massive datasets from experiments, clinical trials, and literature reviews. AI algorithms can sift through this data and identify hidden patterns or correlations that may not be immediately apparent to human researchers. This increases the chances of finding promising drug candidates that may have been overlooked otherwise.

Also, AI can assist in optimizing drug compounds. After identifying potential drug candidates, AI algorithms can analyze their chemical structures and predict their properties, such as solubility, stability, and bioavailability. This information helps researchers design and modify compounds to enhance their efficacy and reduce potential side effects. Considering the complexity of analyzing and interpreting large datasets, pharmaceutical companies often choose to hire a data scientist specializing in AI and drug discovery to maximize the efficiency and accuracy of their research efforts.