AI Creates Cancer Cure in Only 30 Days

AI Creates Cancer Cure in Only 30 Days

Due to the slow, expensive, and constrained nature of the traditional trial-and-error approach, AI is quickly changing how drugs and medicines are discovered and developed.

And sure enough, in just 30 days, artificial intelligence was able to predict a patient’s chance of survival and create a successful cancer treatment for them as well.

University of Toronto and Insilico Medicine scientists created a possible treatment for HCC (hepatocellular carcinoma) using an AI drug discovery platform known as Pharma.AI in a newer study that was published in the journal Chemical Science.

According to Cleveland Clinic, the most prevalent form of liver cancer, HCC, develops when a tumor develops on the liver.

Researchers used Pharma.AI’s AlphaFold, an AI conducted protein structure database, to find a new target for cancer treatment that had not yet been discovered. They then created a “novel hit molecule” that could bind to that target all on its own.

The potential drug was developed in just 30 days, starting with the choice of the target and only requiring the synthesis of seven compounds.

They found a more powerful hit molecule after producing compounds again, but it’s important to note that any potential drug would need to undergo clinical trials before being used widely.

Founder and CEO of Insilico Medicine, Alex Zhavoronkov, stated that “While the world was fascinated with all the advances in generative AI in art and language, our AI algorithms managed to design some potent inhibitors of a target with an AlphaFold derived structure.”

Nobel Prize winner Michael Levitt, noted that “This paper is just further evidence for the capacity of AI to transform the drug creation process with enhanced speed, efficiency, and overall accuracy. Bringing together the predictive capacity of AlphaFold and target and drug design capacity of Insilico Medicine’s Pharma. AI platform, it is possible to imagine that we are right on the cusp of a new era of AI powered drug discovery. Our AI algorithms managed to design some potent inhibitors of a target with an AlphaFold derived structure.”

This comes after AlphaFold, which predicted protein structure for the entire human genome in 2022, made a significant advancement in structural biology and AI.

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

The pipeline combines AlphaFold with Insilico Medicine end-to-end, as well as PandaOmics and Chemistry42, AI-powered drug discovery platforms, in the search for new treatments for hepatocellular carcinoma.

To find a new target, researchers used Pharma.AI and AlphaFold, an AI powered protein structure database.

Furthermore, a professor of chemistry and computer science by the name of Alan Aspuru-Guzik, mentioned that “What this paper proves is that for health care, AI developments are more than their parts’ sum. If one uses a generative model targeting an AI derived protein, one can also substantially expand the range of diseases we can target. If one adds self driving labs to the mix, we’ll be in uncharted territory. Stay tuned!”

The good news is that AI may be a cutting-edge technology for cancer treatment in the future, with potential applications in cancer clinics all over the world so look forward to a new era of medical advancements the likes of which we have never seen in our lives!


Katherine is just getting her start as a journalist. She attended a technical school while still in high school where she learned a variety of skills, from photography to nutrition. Her enthusiasm for both natural and human sciences is real so she particularly enjoys covering topics on medicine and the environment.

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