AI Model Accurately Predicts Patient Response to Drug Compounds

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."