According to the online journal Pharmaceutical Technology, machine learning engineers are increasingly sought after by pharmaceutical companies. The largest number of job openings are found in North America, but some countries in Europe and Asia are gaining ground. France, for example, has seen the most growth with a 2% increase in job openings between the beginning of March and May 2022, compared to the same period last year, reaching 4.2% of global openings.
Given its potential to rapidly assimilate megadata from biomedical databases, AI is increasingly being used in the pharmaceutical field, particularly to improve computer-aided drug design. According to GlobalData, the data and analytics company from which Pharmaceutical Technology published this report on ML job opportunities, it can significantly reduce the time and cost of bringing a drug to market, especially in areas of unmet need, such as rare diseases.
GlobalData's latest report, "Artificial Intelligence (AI) in Drug Discovery - Thematic Research," reveals that total pharmaceutical industry spending on AI is expected to reach over $3 billion by 2025.
Kitty Whitney, senior director of thematic analysis at GlobalData, states:
"Drug discovery and development is an incredibly expensive and time-consuming process. The time it takes 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 include target identification and validation, assay development and screening, outcome identification, lead optimization and selection of candidates for further clinical development. The overall process takes several months and often results in low success rates or poor quality results."Over the past few decades, advances in computational technologies have led to the discovery of new drugs but only 10% of candidates qualified for clinical trials, AI has changed that. Kitty Whitney comments:
"AI has shown enormous potential to further improve these methods by rapidly ingesting and exploring the expanding chemical space, driven by the ever-increasing amount of biomedical big data, such as genomics, for which some conventional approaches are not suitable. Machine learning algorithms have been successfully used for drug target identification, virtual compound screening, de novo drug design, drug reuse, and identification of biomarkers of treatment response."In recent years, the use of AI has led to the emergence of an ever-increasing number of start-ups operating in the pharmaceutical field, drug discovery partnerships, and record investments. This has seen the first AI-developed drug enter clinical trials and the reuse of a previously marketed drug to treat COVID-19. An analysis of GlobalData's Deals database shows that the number of AI-based strategic drug discovery alliances has increased significantly, from just 10 in 2015 to 105 in 2021, including nearly 70 with pharmaceutical companies. Among the top AI providers, GlobalData cites BenevolentAI, Exscientia, Insilico Medicine, Recursion Pharmaceuticals, Atomwise, and as top AI users, Janssen, AstraZeneca, Pfizer, Bayer, Bristol Myers Squibb, GSK, Sanofi and Takeda. However Kitty Whitney warns:
"Although AI has shown the potential to significantly transform drug discovery processes, its use is still in its infancy. Most new drugs that have been developed using AI are in preclinical or discovery stages, and it could be many years before an AI-based treatment is approved. Despite the promise that AI holds for drug discovery, it still faces several challenges. These include data quality and relevance, educating the scientific community to increase buy-in, controlling the hype or dominant narrative around AI, and skill shortages."