AI-powered drug discovery captures pharma interest

A drug-hunting deal inked last month, between Numerate, of San Bruno, California, and Takeda Pharmaceutical to use Numerate’s artificial intelligence (AI) suite to discover small-molecule therapies for oncology, gastroenterology and central nervous system disorders, is the latest in a growing number of research alliances involving AI-powered computational drug development firms. Also last month, GNS Healthcare of Cambridge, Massachusetts announced a deal with Roche subsidiary Genentech of South San Francisco, California to use GNS’s AI platform to better understand what affects the efficacy of known therapies in oncology. In May, Exscientia of Dundee, Scotland, signed a deal with Parisbased Sanofi that includes up to €250 ($280) million in milestone payments. Exscientia will provide the compound design and Sanofi the chemical synthesis of new drugs for diabetes and cardiovascular disease. The trend indicates that the pharma industry’s long-running skepticism about AI is softening into genuine interest, driven by AI’s promise to address the industry’s principal pain point: clinical failure rates. The industry’s willingness to consider AI approaches reflects the reality that drug discovery is laborious, time consuming and not particularly effective. A two-decade-long downward trend in clinical success rates has only recently improved (Nat. Rev. Drug Disc. 15, 379–380, 2016). Still, today, only about one in ten drugs that enter phase 1 clinical trials reaches patients. Half those failures are due to a lack of efficacy, says Jackie Hunter, CEO of BenevolentBio, a division of BenevolentAI of London. “That tells you we’re not picking the right targets,” she says. “Even a 5 or 10% reduction in efficacy failure would be amazing.” Hunter’s views on AI in drug discovery are featured in Ernst & Young’s Biotechnology Report 2017 released last month. Companies that have been watching AI from the sidelines are now jumping in. The bestknown machine-learning model for drug discovery is perhaps IBM’s Watson. IBM signed a deal in December 2016 with Pfizer to aid the pharma giant’s immuno-oncology drug discovery efforts, adding to a string of previous deals in the biopharma space (Nat. Biotechnol. 33, 1219– 1220, 2015). IBM’s Watson hunts for drugs by sorting through vast amounts of textual data to provide quick analyses, and tests hypotheses by sorting through massive amounts of laboratory data, clinical reports and scientific publications. BenevolentAI takes a similar approach with algorithms that mine the research literature and proprietary research databases. The explosion of biomedical data has driven much of industry’s interest in AI (Table 1). The confluence of ever-increasing computational horsepower and the proliferation of large data sets has prompted scientists to seek learning algorithms that can help them navigate such massive volumes of information. A lot of the excitement about AI in drug discovery has spilled over from other fields. Machine vision, which allows, among other things, self-driving cars, and language processing have given rise to sophisticated multilevel artificial neural networks known as deeplearning algorithms that can be used to model biological processes from assay data as well as textual data. In the past people didn’t have enough data to properly train deep-learning algorithms, says Mark Gerstein, a biomedical informatics professor at Yale University in New Haven, Connecticut. Now researchers have been able to build massive databases and harness them with these algorithms, he says. “I think that excitement is justified.” Numerate is one of a growing number of AI companies founded to take advantage of that data onslaught as applied to drug discovery. “We apply AI to chemical design at every stage,” says Guido Lanza, Numerate’s CEO. It will provide Tokyo-based Takeda with candidates for clinical trials by virtual compound screenings against targets, designing and optimizing compounds, and modeling absorption, distribution, metabolism and excretion, and toxicity. The agreement includes undisclosed milestone payments and royalties. Academic laboratories are also embracing AI tools. In April, Atomwise of San Francisco launched its Artificial Intelligence Molecular Screen awards program, which will deliver 72 potentially therapeutic compounds to as many as 100 university research labs at no charge. Atomwise is a University of Toronto spinout that in 2015 secured an alliance with Merck of Kenilworth, New Jersey. For this new endeavor, it will screen 10 million molecules using its AtomNet platform to provide each lab with 72 compounds aimed at a specific target of the laboratory’s choosing. The Japanese government launched in 2016 a research consortium centered on using Japan’s K supercomputer to ramp up drug discovery efficiency across dozens of local companies and institutions. Among those involved are Takeda and tech giants Fujitsu of Tokyo, Japan, and NEC, also of Tokyo, as well as Kyoto University Hospital and Riken, Japan’s National Research and Development Institute, which will provide clinical data. Deep learning is starting to gain acolytes in the drug discovery space. K TS D ES IG N /S ci en ce P ho to L ib ra ry NEWS