AI develops cancer drug in 30 days

By Stacy Liberatore for

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

Artificial intelligence has developed a treatment for an aggressive cancer in just 30 days and shown that it can predict a patient’s chance of survival using doctor’s notes.

The breakthroughs were achieved by separate systems, but show how the use of the powerful technology goes far beyond image and text generation.

Researchers at the University of Toronto teamed up with Insilico Medicine to develop a potential treatment for hepatocellular carcinoma (HCC) using an AI drug discovery platform called Pharma.

HCC is a form of liver cancer, but the AI ​​discovered a previously unknown treatment route and designed a “new hit molecule” that could bind to that target.

The system, which can also predict survival rate, was invented by scientists at the University of British Columbia and BC Cancer, who found the model to be 80 percent accurate.

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

AI will be the new weapon against deadly diseases as the technology is able to analyze massive amounts of data, reveal patterns and relationships and predict treatment effects.

Insilico Medicine founder and CEO Alex Zhavoronkov said in a statement, “While the world was fascinated by the advancements in generative AI in art and language, our generative AI algorithms succeeded in designing powerful 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 liver cancer.

The feat was achieved in just 30 days from target selection and after synthesizing only seven compounds.

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

Feng Ren, Chief Scientific Officer and Co-CEO of Insilico Medicine, said: “AlphaFold broke new scientific ground by 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 to generate new therapies to address diseases with high unmet need. This paper is an important first step in that direction.’

Another AI system identified characteristics unique to each patient and predicted six-month, 36-month, and 60-month survival with greater than 80 percent accuracy

READ MORE: AI X-ray screening tool twice as effective in detecting lung cancer as doctors, study suggests

In a real-world setting, machine learning-based software has greatly 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 notes from the oncologist after a patient’s initial consultation.

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

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

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

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

Traditionally, cancer survival rates are retrospectively calculated and categorized based on only a few generic factors, such as cancer site and tissue type.

However, the model can pick up unique clues in a patient’s initial consultation document to provide a more nuanced assessment.

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

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

‘[But] the beauty of neural NLP models is that they are highly scalable and portable and do not require structured data sets. We can quickly train these models using local data to improve performance in a new region.”

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