Artificial Intelligence (AI) has the potential to treat liver cancer within a month.
Researchers at the University of Toronto, along with Insilico Medicine, have developed a potential treatment for hepatocellular carcinoma (HCC) using an artificial intelligence drug discovery platform called Pharma.AI.
HCC is the most common type of liver cancer and occurs when a tumor grows on the liver. The researchers applied AlphaFold, an AI-powered protein structure database, to Pharma.AI to uncover a novel target, a previously unknown treatment pathway, for cancer and developed a "novel hit molecule" that could bind to that target without aid. The creation of the potential drug was accomplished in just 30 days from the selection of the target and after synthesizing just seven compounds.
After a second round of generating compounds, they discovered a more potent hit molecule, but any potential drug would need to go through clinical trials before widespread use. The study was published in the journal Chemical Science.
The traditional method of drug discovery using trial and error is slow, expensive, and limits the scope of exploration. With the use of AI, researchers can enhance speed, efficiency, and accuracy in the drug discovery process. In the words of Nobel Prize winner in chemistry, Michael Levitt, "This paper is further evidence of the capacity for AI to transform the drug discovery process with enhanced speed, efficiency, and accuracy."
AlphaFold made a huge breakthrough in both AI and structural biology by predicting protein structure for the whole human genome. AlphaFold broke new scientific ground in 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 in order to generate novel therapeutics to tackle diseases with high unmet need. This paper is an important first step in that direction, says co-author Feng Ren, chief scientific officer and co-CEO of Insilico Medicine.
Another study, published in the journal JAMA Network Open, showed that an AI system invented by scientists at the University of British Columbia and BC Cancer was able to predict cancer patient survival rates using doctors' notes. The model uses natural language processing (NLP), which is a part of AI that can understand complex human language. The NLP can analyze doctors' notes after an initial consultation visit and identify individual characteristics specifically for each patient.
It was able to predict six-month, 36-month, and 60-month survival with an accuracy rate of over 80%. This model can also determine rates for all cancers, while previous models were only able to apply to certain cancer types. "The AI essentially reads the consultation document similar to how a human would read it," says lead author Dr. John-Jose Nunez, a psychiatrist and clinical research fellow with the UBC Mood Disorders Centre and BC Cancer. "These documents have many details like the age of the patient, the type of cancer, underlying health conditions, past substance use, and family histories. The AI brings all of this together to paint a more complete picture of patient outcomes."
Cancer survival rates are traditionally calculated retrospectively and only categorized by a few generic factors such as tissue type and cancer site. This model was tested using data from 47,625 patients across six BC cancer sites located in British Columbia. "Because the model is trained on BC data, that makes it a potentially powerful tool for predicting cancer survival here in the province," Nunez said. "The great thing about 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. I would suspect that these models provide a good foundation anywhere in the world where patients are able to see an oncologist."
AI could be a game-changing technology in the field of cancer research and treatment. With its ability to analyze large amounts of data and identify patterns that may not be easily discernible to human experts, AI has the potential to revolutionize the way cancer is diagnosed, treated, and managed.
One area where AI is showing promise is in drug discovery. Traditional drug discovery methods are time-consuming, expensive, and often rely on trial-and-error approaches. AI-powered drug discovery platforms, on the other hand, can analyze vast amounts of data and simulate the effects of millions of compounds on specific targets. This can greatly speed up the drug discovery process and lead to the development of more effective and targeted cancer treatments.
In the recent study published in Chemical Science, researchers at the University of Toronto and Insilico Medicine developed a potential treatment for hepatocellular carcinoma (HCC) with an AI drug discovery platform called Pharma.AI. By using AlphaFold, an AI-powered protein structure database, the researchers were able to uncover a novel target – a previously unknown treatment pathway – for cancer and developed a “novel hit molecule” that could bind to that target without aid. The potential drug was created in just 30 days from the selection of the target and after synthesizing just seven compounds.
While this is just the beginning, researchers and experts are optimistic about the potential of AI in drug discovery. “This paper is further evidence of the capacity for AI to transform the drug discovery process with enhanced speed, efficiency, and accuracy,” said Michael Levitt, a Nobel Prize winner in chemistry. “Bringing together the predictive power of AlphaFold and the target and drug-design power of Insilico Medicine’s Pharma.AI platform, it’s possible to imagine that we’re on the cusp of a new era of AI-powered drug discovery.”
AI is also being used to improve cancer diagnosis and treatment. For example, researchers at the University of British Columbia and BC Cancer have developed an AI system that can predict cancer patient survival rates using doctors’ notes. The model uses natural language processing (NLP), which is a part of AI that can understand complex human language. The NLP can analyze doctors’ notes after an initial consultation visit and identify individual characteristics specifically for each patient. It was able to predict six-month, 36-month, and 60-month survival with an accuracy rate of over 80%. This model can also determine rates for all cancers, while previous models were only able to apply to certain cancer types.
The ability to predict cancer patient survival rates could be a game-changer in cancer care, as it could help doctors make more informed treatment decisions and offer personalized care to patients. The AI system could also potentially be used to improve cancer care in developing countries or areas with limited access to healthcare professionals.
In addition to drug discovery and diagnosis, AI is also being used to improve cancer treatment. Researchers at the University of Texas MD Anderson Cancer Center have developed an AI system that can predict how different treatments will affect cancer patients.
AI has the potential to revolutionize cancer care in the future, and can be implemented in cancer clinics globally. One crucial aspect of improving cancer care is predicting survival rates, which can enable healthcare providers to offer timely support services and aggressive treatment options. By using a tool like this, cancer care can be personalized and optimized to give patients the best possible outcome. This has the potential to significantly enhance patient care and treatment effectiveness.