Efforts to use artificial intelligence to discover drugs have been underway for about a decade but industry watchers are predicting an inflection point is nearing for investors, who have been looking for ways to determine how AI-first drug developers should be valued. AI and machine learning offer the potential to speed up the hunt for new therapies by more quickly identifying compounds to treat disease. There’s also the promise of making clinical trial phases more efficient by improving patient enrollment and processing insights quickly as the information rolls in from studies. More tangible evidence of these capabilities is now being demonstrated. A high-profile example has been the effort to fight Covid-19, which forced biotech and pharmaceutical companies to bring all their capabilities to the effort of discovering vaccines and treatments in record time. Lidia Fonseca, Pfizer’s chief digital and technology officer, has discussed the role the pandemic has played in accelerating digital advancements during several conference appearances over the past year. “We believe that Covid-19 has advanced these trends by as much as five years,” Fonseca said in a virtual fireside chat with McKinsey in January . ” It’s not so much that these are new technologies, more that we are applying them at scale.” Key points for investors By Deloitte’s latest estimates , it can cost $2 billion to develop a new drug. Artificial intelligence and machine learning promise to lower that cost by reducing development times and increasing success rates. More advanced algorithms, increased computing power and richer data sets are leading to more progress. While most biotech and pharmaceutical companies are using AI and machine learning tools, companies that are native to the space are about to hit an inflection point that will help investors value these companies. Boston Consulting Group said in March that AI-first drug developers have identified more than 150 small-molecule drugs , with at least 15 already in clinical trials. The capabilities that will occur when quantum computing is widely adopted are unimaginable now, Fonseca added. But even with today’s supercomputing power, Pfizer is able to use modeling and simulation to screen millions of compounds to arrive at potential drug targets. The development of Paxlovid , an oral Covid treatment, in four months was helped along by deploying various machine learning techniques, Pfizer has said. ‘A great convergence’ A “great convergence” is underway throughout the industry, according to Julia Angeles, the portfolio manager of Baillie Gifford’s Health Innovation Fund. “It’s not just one technology that comes to play a role. It’s actually a combination of technologies,” Angeles said. In an interview, she detailed a number of improvements that have occurred with the advanced algorithms used to power machine learning, the richness of the data sets that can be examined for information and the efficacy of the computing power that is needed to bring it all together. But the critical change is the scale at which it is being done, Angeles said. “Many more companies can do it,” she said. “We have much more relevant data to mine biology, and we have much more powerful computers to do it much more effectively, and much faster than we’ve done it in the past.” One key component has been a steep drop in the cost of sequencing genomic data over the past 10 years, which has resulted in a trove of patient information that can be combined with other types of electronic health records. Separately, the release last year of source code for AlphaFold2 by DeepMind, the UK-based AI venture owned by Alphabet , has helped visualize …….
Artificial intelligence drug hunters are nearing a pivotal stage in their development – CNBC
