AI Could Develop Cancer Treatments in Less Than a Month

Researchers at the University of Toronto have partnered with Insilico Medicine to use AI-powered tools to design a potential treatment for liver cancer. The team used AlphaFold and Pharma.AI to identify a previously undiscovered pathway for treating hepatocellular carcinoma (HCC).

Artificial intelligence has revolutionized our world, transforming the way we live, work, and play. It has particularly brought about significant advancements in healthcare, enabling personalized care, efficient solutions, and accurate diagnoses. 

AI is present in many aspects of our lives, from Siri to ChatGPT, and its application to healthcare is only increasing.

Insilico Medicine and U of T Acceleration Consortium Join Forces

Insilico Medicine, a multinational biotechnology company, has partnered with the University of Toronto’s Acceleration Consortium, led by Alán Aspuru-Guzik, to create AI labs that help accelerate the design and development of research.

In a groundbreaking study published in the Chemical Science journal in January 2023, researchers have designed a potential treatment for hepatocellular carcinoma (HCC), a common and aggressive form of liver cancer that claims 700,000 lives each year.

AI-Powered Protein Structure Database AlphaFold and Pharma.AI Drug Discovery Platform

AlphaFold is an AI-powered protein structure database, and this work is the first to apply it in an end-to-end artificial intelligence drug development platform dubbed Pharma.AI. 

This platform has robust biocomputational and generative chemistry engines, which enable the tool to test millions of possible drug and route combinations. These engines provide the platform with the ability to examine a wide range of combinations. 

By using these technologies, the researchers were able to locate a vulnerable region of the CDK enzyme that had not been observed before.

Identifying CDK-20 Enzymatic Protein and Targeting Cancer Growth

The CDK enzyme is a fundamental protein in the cell cycle, which facilitates the growth and division of cells. In cancer cells, cellular production continues even though the cell is not fit to multiply, leading to the formation of tumors. 

By targeting the CDK enzymes, the cell cycle cannot continue in the unhealthy cell, slowing down the production of cancerous cells, and ultimately, stopping it altogether.

Clinical Trials and the Future of AI-Driven Cancer Treatments

The medicine that was developed as a consequence of this study was tested on living cells, and the results showed that it significantly reduced the development of cancer. 

In just 30 days, an #AI system developed by DeepMind, known as AlphaFold, has identified a potential drug candidate for liver cancer. AlphaFold uses deep learning to predict 3D models of protein structures, a process that would typically take human scientists decades. pic.twitter.com/y7vSLzwTlb

— Realtime Global Data Intelligence Platform (@KIDataApp) January 27, 2023

Nevertheless, before it can really be used, it will need need to be tested in clinical settings. The application of artificial intelligence has significantly cut down on the amount of time and effort required for such a study. 

What used to take years of trial and error has been reduced to just a few weeks of AI-powered discoveries.

The Double-Edged Sword of the Pharmaceutical Industry

As we get closer to the possibility of therapies, we must continue to exercise extreme caution about the pharmaceutical sector. Because of high demand, the cost of life-sustaining medications and treatments may skyrocket, rendering them unaffordable for a significant portion of the population.

It is still unknown whether or not these challenges can be surmounted with regard to cancer research that is driven by AI.

AI-Driven Cancer Treatment – a Potential Cure for Tomorrow

The application of artificial intelligence (AI) in cancer treatment has enormous potential, and despite the fact that there are obstacles to surmount, we can maintain a level of optimism that these developments could lead to treatments that are more widely available to all patients, regardless of their socioeconomic status.