Artificial intelligence (AI) designed a potential treatment for an aggressive cancer which kills 700,000 people a year and it took just 30 days.
University of Toronto researchers worked with Insilico Medicine to develop potential treatment for hepatocellular carcinoma (HCC), the university’s Varsity newspaper reported.
Without AI, scientists rely on conventional trial and error methods of chemistry that are slow, expensive and limit the scope of their exploration.
The AI designed and ‘tested’ the new drug, coming up with an improvement as it did so.
In 2022, the AlphaFold computer programme, developed by DeepMind, owned by Google's parent company Alphabet, predicted protein structures for the whole human genome.
The 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.
AlphaFold was successfully applied to an end-to-end AI-powered drug discovery platform called Pharma.AI, including a biocomputational engine, PandaOmics and a generative chemistry engine, Chemistry42.
Researchers 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.
This was accomplished in just 30 days from target selection. In a second round of AI-powered compound generation, researchers discovered a more potent hit molecule.
Alex Zhavoronkov, founder and CEO of Insilico Medicine, said: "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."
Feng Ren, co-author, chief scientific officer and co-CEO of Insilico Medicine wrote, "AlphaFold broke new scientific ground in predicting the structure of all proteins in the human body.
"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."