Efforts to use artificial intelligence for drug discovery have been underway for about a decade, but industry watchers predict a tipping point is near 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 accelerate the search for new therapies by faster identifying compounds to treat diseases. There is also promise to make clinical trial phases more efficient by improving patient recruitment and processing findings quickly as trial information arrives. More concrete evidence of these abilities will now be demonstrated. A prominent example was the effort to combat Covid-19, which forced biotech and pharmaceutical companies to bring all their skills to the effort to discover 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 progress during several conference appearances over the past year. “We believe that Covid-19 has pushed these trends by up to five years,” Fonseca said in a virtual fireside chat with McKinsey in January. “It’s not so much that these are new technologies, it’s that we are applying them on a large scale.” Important points for investors According to the latest estimates from Deloitte, the development of a new drug can cost US$ 2 billion. Artificial intelligence and machine learning promise to reduce these costs by reducing development times and increasing success rates. More advanced algorithms, increased computing power, and richer datasets lead to more progress. While most biotech and pharmaceutical companies use AI and machine learning tools, companies native to this space are on the verge of reaching an inflection point that will help investors evaluate these companies. The Boston Consulting Group said in March that AI-First drug developers have identified more than 150 small molecule drugs, at least 15 of which are already in clinical trials. The capabilities that will emerge when quantum computing becomes widespread are now unimaginable, 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 aided by the use of various machine learning techniques, Pfizer said. “A great convergence” According to Julia Angeles, portfolio manager of Baillie Gifford’s Health Innovation Fund, there is a “great convergence” happening across the industry. “It’s not just technology that matters. It’s actually a combination of technologies,” Angeles said. In an interview, she detailed a number of improvements that have occurred in the advanced machine learning algorithms, the richness of the data sets that can be examined for information, and the efficiency of the computing power required to bring it all together. But the critical change is the extent to which it’s being done, Angeles said. “A lot more companies can do that,” she said. “We have much more relevant data for mining biology, and we have much more powerful computers to do it much more effectively and much faster than we have in the past.” A key component has been a sharp drop in the cost of sequencing genomic data in over the past 10 years, resulting in a wealth of patient information that can be combined with other types of electronic health records. Separately, the release of the source code for AlphaFold2 by Alphabet’s UK-based AI company DeepMind last year helped visualize the structure of proteins, which should also help development in this area for years to come. So far, technological advances have led to a wave of small molecule drugs being developed by AI-native drug discovery companies. By combing through public records, the Boston Consulting Group has identified more than 150 small molecule drugs from the industry leaders, with at least 15 already in clinical trials. BCG said the pipeline is growing at nearly 40% annually. “Are these working in the clinic? We’ll have to wait and see. Hopefully they do. Because if they do, if they work as well as man-discovered drugs, that would be very exciting,” said Chris Meier, an executive director and Partners at BCG. “Of course, if the success rate comes back much better, it will be very exciting because suddenly we have something that is better than humans. We don’t know yet,” he said. Expected updates from a number of drug candidates over the next 12 to 18 months was a key reason Morgan Stanley analysts said they expect the sector to be close to a turning point. In a research note published in late June, Morgan Stanley said readings from early clinical work will help the market assign value to AI-native drug stocks. According to the report, investors have debated in the past whether the group should have valuations of a technology platform or a biotech company. In fact, the business models of these companies can vary. Some are more akin to the software-as-a-service model, where companies make machine learning capabilities available to partners for a fee. But many also develop their own standalone projects and have collaborations with pharmaceutical companies, where they receive milestone payments and royalties if the compounds hit targets and are commercialized. The Value of Failing Fast According to the latest estimates from Deloitte, it can cost US$2 billion to develop a new drug. This number accounts for the vast majority of compounds studied, but they fail in early clinical trials. Success rates can be under 5% and development times can be a decade or more. Morgan Stanley analysts estimate that improving the pace of preclinical and phase 1 development by about 2% could propel the industry to develop about 50 novel therapies over the next 10 years. This could equate to an NPV of about $50 billion for the biopharmaceutical industry, they said. One of the key ways AI-powered drug discovery can save money is by identifying the molecules with the greatest and least likelihood of success early in the research cycle. This greatly reduces downtime costs. Robert Burns, a managing director at HC Wainwright, said Schrödinger described a 10-month time frame to identify a development candidate, while Exscientia put his average time at around 12 months. In comparison, traditional drug research can take three to five years. “That’s important, especially since you know that many of these companies in the big pharma and biotech companies have very similar goals,” Burns said. Speed can not only save money but also provide a competitive advantage. Despite the promises these companies are delivering, the stocks have fallen sharply along with the rest of the biotech sector. Most are now trading below their IPO prices. Baillie Gifford’s Health Innovation Fund reflects this trend. It’s down more than 26% year-to-date, according to FactSet, but is up nearly 7% so far this month. Within the AI-First space, Angeles owns Exscientia and Recursion Pharmaceuticals, although neither are among the fund’s top holdings. Exscientia shares are down 39% year to date and are trading 45% below their opening price last September. The company has collaborations with the Bill & Melinda Gates Foundation, Bayer, Sanofi, Bristol-Myers Squibb and others. Oncology immunotherapy drug EXS-21546 is Exscientia’s lead compound. It is in Phase 1b/2 trials to test the drug in patients with solid tumors. Recursion Pharmaceuticals has lost about 45% of its value since its IPO in April 2021. It is very focused on using imaging technology to discover drug targets, and much of its focus has been on rare diseases. It has partnerships with Bayer, Roche and Takeda and is in a phase 2 clinical trial for the treatment of cerebral cavernous malformations, a disease of the blood vessels in the brain that can lead to seizures and fatal bleeding in the brain. Burns has a buy rating on Relay Therapeutics, which is down about 35% so far this year and is trading just below its IPO price of $20. The company has multiple treatments in the pipeline for breast cancer, and data for its lead compound RLY-4008 should be released by the end of this year. Its partners include Roche and Genentech. On Thursday, Relay announced it had sufficient funding to support its plan of operations through at least 2025. As of June 30, its cash and investments totaled approximately $838 million, compared to $958 million at the end of 2021. Schrodinger reported that it had $513 million in cash, cash equivalents, restricted cash and marketable securities as of June 30 versus $529 million as of March 31. At the end of the first quarter, Exscientia had approximately $719.8 million in cash, while Recursion was $591.1 million as of March 31. Until these companies offer updates on these programs, the investment case depends on the potential value of the companies’ platforms. Once investors can see progress in clinical trials, confidence will increase. “I think there really needs to be some sort of validation here,” Burns said.