Researchers at the CUNY Graduate Center have created an artificial intelligence model, Context-aware Deconfounding Autoencoder (CODE-AE), that can screen drug compounds to accurately predict efficacy in humans. In tests, the model was able to theoretically identify personalized drugs that could better treat more than 9,000 cancer patients. The researchers expect the technique will improving the accuracy and reduce the time and cost of drug discovery and development, and accelerate precision medicine. "Our new machine learning model can address the translational challenge from disease models to humans," said Lei Xie, PhD, a professor of computer science, biology and biochemistry at the CUNY Graduate Center and Hunter College. "CODE-AE uses biology-inspired design and takes advantage of several recent advances in machine learning. For example, one of its components uses similar techniques in Deepfake image generation."

A novel artificial intelligence model could significantly improve the accuracy and reduce the time and cost of the drug development process. Between identifying a potential therapeutic compound and U. S. Food and Drug Administration (FDA) approval of a new drug is an arduous journey that can take well over a decade and cost upwards of a billion dollars. A team of researchers at the CUNY Graduate Center has developed a novel artificial intelligence model that could significantly improve the accuracy and reduce the time and cost of the drug development process. As described in a paper to be published today (October 17) in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions.