����ֱ��

In less than a month, researchers have used AlphaFold, an artificial intelligence (AI)-powered protein structure database, to design and synthesize a potential drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer.

The researchers successfully applied AlphaFold to an end-to-end AI-powered drug discovery platform called Pharma.AI. That included a biocomputational engine, PandaOmics, and a generative chemistry engine, Chemistry42. They discovered a novel target for HCC – a previously undiscovered treatment pathway – and developed a “novel hit molecule” that could bind to that target without the aid of an experimentally determined structure. The feat was accomplished in just 30 days from target selection and after only synthesizing seven compounds.

In a second round of AI-powered compound generation, researchers discovered a more potent hit molecule – although any potential drug would still need to undergo clinical trials.

The study –  – is led by the University of Toronto  Director Alán Aspuru-Guzik, Nobel laureate Michael Levitt and  founder and CEO Alex Zhavoronkov.

“While the world was fascinated with advances in generative AI in art and language, our generative AI algorithms managed to design potent inhibitors of a target with an AlphaFold-derived structure,” Zhavoronkov said.

“AlphaFold broke new scientific ground in predicting the structure of all proteins in the human body,” added co-author Feng Ren, chief scientific officer and co-CEO of Insilico Medicine. “At Insilico Medicine, we saw that as an incredible opportunity to take these structures and apply them to our end-to-end AI platform in order to generate novel therapeutics to tackle diseases with high unmet need. This paper is an important first step in that direction.”

AI is revolutionizing drug discovery and development. In 2022, the AlphaFold computer program, developed by Alphabet’s DeepMind, predicted protein structures for the whole human genome – a remarkable breakthrough in both AI applications and structural biology. This free AI-powered database is helping scientists predict the structure of millions of unknown proteins, which is key to accelerating the development of new medicines to treat disease and beyond.

Scientists have traditionally relied on conventional trial-and-error methods of chemistry that are slow, expensive and limit the scope of their exploration of new medicines. As COVID-19 has demonstrated, the speedy development of new drugs or new formulations of existing ones is needed – and increasingly expected by the public. AI has the potential to deliver this speed by transforming materials and molecular discovery, as it has done with just about every branch of science and engineering over the last decade.

“This paper is further evidence of the capacity for AI to transform the drug discovery process with enhanced speed, efficiency, and accuracy,” said Michael Levitt, a Nobel Prize winner in chemistry and the Robert W. and Vivian K. Cahill Professor of Cancer Research and professor of computer science at Stanford University. “Bringing together the predictive power of AlphaFold and the target and drug-design power of Insilico Medicine’s Pharma.AI platform, it’s possible to imagine that we’re on the cusp of a new era of AI-powered drug discovery.”

Both Insilico Medicine – a clinical stage company that counts both Aspuru-Guzik and Levitt as advisers – and ����ֱ��’s Acceleration Consortium are working actively to develop self-driving laboratories, an emerging technology that combines AI, automation and advanced computing to accelerate materials and molecular discovery. Accessible tools and data will help more scientists enter the field of AI for science, in turn helping to drive major progress in this area.  

“What this paper demonstrates is that for health care, AI developments are more than the sum of their parts,” said Aspuru-Guzik, a professor of chemistry and computer science in ����ֱ��’s Faculty of Arts & Science and the Canada 150 Research Chair in Theoretical and Quantum Chemistry. “If one uses a generative model targeting an AI-derived protein, one can substantially expand the range of diseases that we can target. If one adds self-driving labs to the mix, we will be in uncharted territory. Stay tuned!”