AI develops cancer drug in 30 days

By Stacy Liberatore for

13:02 19 Mar 2023, update 13:26 19 Mar 2023

Artificial intelligence has developed a cure for an aggressive form of cancer in just 30 days and has been shown to predict a patient’s survival rate using doctors’ scores.

The breakthroughs were made by separate systems, but show how the uses of the powerful technology go far beyond generating images and text.

University of Toronto researchers worked with Insilico Medicine to develop a potential treatment for hepatocellular carcinoma (HCC) using an AI-based drug discovery platform called Pharma.

HCC is a form of liver cancer, but AI has discovered a previously unknown treatment pathway and engineered a “new blockbuster molecule” that could bind to this target.

The system, which can also predict survival rate, is the brainchild of scientists at the University of British Columbia and BC Cancer, who found the model to be 80% accurate.

AI developed cancer treatment (stock) in just 30 days from target selection and after synthesizing just seven compounds

AI is becoming the new weapon against deadly diseases, as the technology is able to analyze large amounts of data, uncover patterns and relationships, and predict the effects of treatments.

Insilico Medicine Founder and CEO Alex Zhavoronkov said in a statement: “While the world was fascinated by advances in generative AI in art and language, our generative AI algorithms succeeded in to design potent inhibitors of a target with an AlphaFold-derived structure.”

The team 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 cancer liver.

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 have discovered a more potent blockbuster molecule – though any potential drug would still have to undergo clinical trials.

Feng Ren, Scientific Director and Co-CEO of Insilico Medicine, said, “AlphaFold broke new ground in predicting the structure of all proteins in the human body.

“At Insilico Medicine, we saw this as an incredible opportunity to take these frameworks and apply them to our end-to-end AI platform to generate new therapies to address diseases with high unmet need. This document is an important first step in that direction.

Another AI system identified characteristics unique to each patient, predicting survival at six months, 36 months and 60 months with greater than 80% accuracy.

READ MORE: AI X-ray screening tool twice as good at finding lung cancer as doctors, study finds

In a real environment, machine learning-based software has significantly improved the identification of lung nodules on chest x-rays.

The system used to predict life expectancy used natural language processing (NLP) – a branch of AI that understands complex human language – to analyze oncologist notes after a patient’s first visit .

The model identified characteristics unique to each patient, predicting six-month, 36-month, and 60-month survival with greater than 80% accuracy.

John-Jose Nunez, a psychiatrist and clinical researcher at the UBC Mood Disorders Center and BC Cancer, said in a statement, “The AI ​​basically reads the consultation document like a human would read it.”

“These documents contain many details such as the patient’s age, type of cancer, underlying health conditions, previous substance use and family history.

“AI combines all of this to paint a complete picture of patient outcomes.”

Traditionally, cancer survival rates have been calculated retrospectively and categorized by only a few generic factors, such as cancer site and tissue type.

The model, however, is able to pick up unique cues from a patient’s initial consultation document to provide a more nuanced assessment.

The AI ​​was trained and tested using data from 47,625 patients at the six BC Cancer sites located in British Columbia.

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

“(But) the advantage of neural NLP models is that they are highly scalable, portable, and don’t require structured data sets. We can quickly train these models using local data to improve performance in a new region.

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