Artificial intelligence developed a treatment for liver cancer in 30 days - ForumDaily
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Artificial intelligence develops treatment for liver cancer in 30 days

Artificial intelligence could be the cutting edge technology for cancer treatment of the future, which can be applied in oncology clinics around the world, reports New York Post.

Photo: IStock

In a new study published in the journal Chemical Science, University of Toronto researchers partnered with Insilico Medicine to develop a potential treatment for hepatocellular carcinoma (HCC) using an AI drug development platform called Pharma.AI.

According to the Cleveland Clinic, HCC is the most common type of liver cancer and occurs when a tumor grows in the liver.

The researchers applied AlphaFold, an AI-based protein structure database, to Pharma.AI to uncover a new target—a previously unknown treatment route—and developed a “new molecule” that could bind to that target unaided.

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The creation of a potential drug was completed in just 30 days from the moment the target was chosen and after the synthesis of only seven compounds.

After a second round of compounding, they found a more powerful molecule, but any potential drug must pass clinical trials before being widely used.

“While the world has been fascinated by advances in generative AI in art and language, our AI algorithms have succeeded in developing powerful target inhibitors with an AlphaFold-derived structure,” Alexey Zhavoronkov, founder and CEO of Insilico Medicine, said in a statement.

AI is rapidly changing the way drugs are discovered and developed, as traditional trial and error is slow, expensive, and limits the scope of research.

“This work is further evidence of the power of AI to transform the drug discovery process with increased speed, efficiency and accuracy,” said Michael Levitt, Nobel Prize winner in chemistry. “By combining the predictive power of AlphaFold with the targeted power and drug discovery capabilities of the Pharma.AI Insilico Medicine platform, we can imagine that we are on the cusp of a new era of AI-assisted drug discovery.”

In 2022, AlphaFold made a huge breakthrough in both artificial intelligence and structural biology by predicting the protein structure for the entire human genome.

"AlphaFold has opened new scientific horizons in predicting the structure of all proteins in the human body," said co-author Feng Ren, chief scientist and co-CEO of Insilico Medicine.

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“At Insilico Medicine, we saw this as an incredible opportunity to take these structures and apply them to our end-to-end artificial intelligence platform to create new therapeutics for high-need diseases. This paper is an important first step in that direction,” he said.

The researchers also explained how various AI information could revolutionize healthcare.

“This paper demonstrates that for healthcare, AI developments are more than the sum of their parts,” said Alan Aspuru-Guzik, professor of chemistry and computer science. “If we use a generative protein-targeting model derived from artificial intelligence, we can significantly expand the range of diseases that we can influence. If you add unmanned laboratories to the mix, we find ourselves in uncharted territory.”

Survival prognosis

A separate study published in the journal JAMA Network Open found that an artificial intelligence system invented by scientists at the University of British Columbia is able to predict the survival of cancer patients using doctors' records. The model uses Natural Language Processing (NLP), which is a piece of AI capable of understanding complex human language.

NLP can analyze doctors' records after the initial visit and identify individual characteristics specifically for each patient.

She was able to predict six-month, 36-month, and 60-month survival rates with over 80% accuracy. This model can also provide indicators for all types of cancer, while previous models could only be applied to certain types of cancer.

“The AI ​​essentially reads the consultation document in the same way a human would read it,” lead author Dr. John-José Nunez, a psychiatrist and research fellow at the UBC Mood Disorders and Cancer Center, said in a statement. “There are many details in these documents, such as the patient's age, type of cancer, underlying health conditions, past substance use, and family history. AI brings it all together to paint a more complete picture of patient outcomes.”

Cancer survival rates are traditionally calculated retrospectively and classified by only a few general factors such as tissue type and cancer location.

“Because the model is trained on cancer data, this makes it a potentially powerful tool for predicting cancer survival here in the province,” Nunes said.

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“The great thing about neural NLP models is that they are highly scalable, portable, and don’t require structured datasets,” he added. “We can quickly train these models using local data to improve performance in a new region.” I suspect these models provide a good basis anywhere in the world where patients might see an oncologist.”

“Predicting cancer survival is an important factor that can be used to improve cancer treatment,” Nunez said. “It can suggest that health care providers refer the patient to support services earlier or suggest a more aggressive treatment option for the patient early. We hope that such a tool can be used to personalize and optimize the treatment a patient receives right away, giving them the best possible outcome.”

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