GlobalData forecasts pharmaceutical industry’s AI spending could exceed US$3B by 2025; AI is being increasingly used to enhance computer-aided drug design, which can cut the time and cost to get a drug to market, particularly in areas of rare diseases

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July 11, 2022 (press release) –

Due to its ability to rapidly assimilate big data from biomedical databases, artificial intelligence (AI) is being increasingly used to enhance computer-aided drug design. This can significantly reduce the time and cost to get a drug to market, particularly in areas of unmet need such as rare diseases, says GlobalData, a leading data and analytics company.

GlobalData’s latest report, ‘Artificial Intelligence (AI) in Drug Discovery – Thematic Research’, reveals that the total spend on AI by the pharma industry is forecast to grow to over $3 billion by 2025.

Kitty Whitney, Senior Director of Thematic Analysis at GlobalData, comments: “Drug discovery and development is an incredibly expensive and time-consuming process. The time needed for a drug to reach the market ranges from 12 to 18 years, with an average cost of about $2.6 billion. Drug discovery processes involve target identification and validation, assay development and screening, hit identification, lead optimization, and the selection of candidates for further clinical development. The overall process takes several months and often results in low hit rates or poor-quality hits.”

Over the past several decades, advances in computational technology have allowed increased exploration of the vast chemical space and are widely used to enhance traditional drug discovery methods to reduce the time and cost of drug development, with significantly higher hit rates. However, success rates are still low, with just 10% of candidates making it into clinical trials.

Whitney adds: “AI has shown enormous potential to further enhance these methods by rapidly ingesting and exploring the expanding chemical space, driven by the ever-growing amount of large biomedical data, such as genomics, which some conventional approaches are not suitable for. Machine learning algorithms have been successfully used for identifying drug targets, virtual screening of compounds, de novo drug design, drug repurposing, and identification of treatment response biomarkers.”

Over the past 3–4 years, there has been increased interest in the use of AI in drug discovery, as witnessed by the emergence of an ever-growing number of start-ups operating in this area, the increasing number of drug discovery partnerships, and record levels of investment. There have also been some recent major milestones, including the first drug developed by AI to enter clinical trials and the repurposing of an already marketed drug to treat COVID-19.

An analysis of GlobalData’s Deals database shows that the number of AI-based drug discovery strategic alliances has increased significantly, from just 10 in 2015 to 105 in 2021, of which almost 70 were with pharma companies. Examples of leading AI vendors include BenevolentAI, Exscientia, Insilico Medicine, Recursion Pharmaceuticals, and Atomwise, while leading adopters of AI include Janssen, AstraZeneca, Pfizer, Bayer, Bristol Myers Squibb, GSK, Sanofi, and Takeda.

Whitney concludes: “While AI has shown the potential to significantly transform drug discovery processes, its use is still in the early stages. Most novel drugs that have been developed using AI are in preclinical or discovery stages, and it could be many years before an AI-based therapy is approved. Despite the promise that AI holds for drug discovery, it still faces several challenges. These include the quality and appropriateness of data, educating the scientific community to increase buy-in, overcoming the hype or mainstream narrative about AI, and skills shortages.”

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Dan Rivard
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