New data suggests the drug industry is hooked on AI.
What’s new: Pharmaceutical companies in several countries are hiring machine learning engineers at increasing rates, industry news publication Pharmaceutical Technology reported. Most job openings are posted in the United States, though some countries in Europe and Asia are gaining ground.
How it works: The publication analyzed data from GlobalData’s paywalled database, which tracks job listings in a variety of industries and analyzes the text to group them into categories.
- 26.4 percent of pharmaceutical companies in the database posted at least one machine learning opening in June 2022, an increase of 2.3 percent over the previous year. Of all the pharma industry jobs posted in June, 1.2 percent were related to machine learning.
- 61 percent of machine learning jobs advertised by pharma companies globally in the three months ending in May were located in the U.S. The Boston, Massachusetts, metropolitan area saw the largest cluster of such jobs followed by the San Francisco Bay Area and San Diego, California.
- The top three European countries — Belgium, France, and the United Kingdom — each represented less than 6 percent of machine learning jobs advertised during the three months ending in May.
- The Asia-Pacific region’s total share decreased 1.9 points in the same time period. Job losses were not consistent across the region, however, China’s share declined from 5 percent to 2 percent, while India’s rose from 5 to 6 percent.
Behind the news: In a recent report, GlobalData estimated that the pharmaceutical industry will spend over $3 billion on AI by 2025, driven largely by applications in drug discovery. The trend has also prompted major pharma companies including Astra-Zeneca, Pfizer, and Sanofi to acquire, invest in, or partner with startups. GlobalData counted 67 such partnerships in 2021, up from 23 in 2018.
Why it matters: Bringing a new drug to market can take decades and cost billions of dollars. AI can cut time and costs in myriad ways, for instance by recognizing viable molecules without lab experimentation, identifying patients who might benefit from a drug, and predicting how patients might respond to them.
We’re thinking: Given the economic value of online advertising and product recommendations, many machine learning engineers — and an entire genre of machine learning approaches — are devoted to optimizing their results. Given the value of pharmaceuticals, we have no doubt that machine learning has immense potential in that domain as well. Similarly, a large body of specialized machine learning techniques is waiting to be developed for many industries.