AI platform models protein flexibility to accelerate drug design

Researchers at the University of Virginia School of Medicine have developed a new artificial intelligence-driven approach to drug discovery that models protein dynamics during molecular binding. The platform, comprising YuelDesign, YuelPocket and YuelBond, uses advanced AI techniques to better predict how drugs interact with proteins within the body. The approach could potentially improve binding predictions and reduce the high failure rates associated with conventional drug development programmes.

Key to the innovation is YuelDesign, which uses diffusion models to generate drug molecules tailored precisely to their protein targets. Unlike conventional methods, which treat proteins as rigid structures, the system accounts for the way proteins naturally flex and change shape during interactions. By treating proteins as flexible rather than static, YuelDesign captures a critical biological phenomenon known as induced fit, where proteins change shape as a drug binds to them.

This allows the system to design both the protein binding site and the corresponding drug molecule simultaneously, enabling them to adapt to each other during development. YuelPocket uses graph neural networks to pinpoint where drugs should attach to proteins, including those predicted using tools such as AlphaFold. YuelBond ensures the chemical structures of the designed molecules are accurate.

The researchers said they showed that, when designing molecules for a well known cancer-related protein called CDK2, only YuelDesign could capture the critical structural changes that happen when a drug binds. Together, the tools could improve both the design of new drugs and the repurposing of existing ones.

Drug development is an expensive and uncertain process, with costs often estimated to exceed billions of dollars and failure rates in human trials approaching 90 percent. A major challenge lies in predicting how drug molecules will bind to their targets, and even small mismatches can make treatments ineffective or lead to harmful side effects. The researchers believe their approach could help reduce the cost of drug development, improve success rates and shorten the time needed to bring new treatments to patients.

The researchers said they have made all of the tools freely available to the scientific community. Their ultimate goal is to make drug discovery faster, cheaper and more likely to succeed so that promising treatments can reach patients sooner.