Using Machine Learning to Speed Up Drug Discovery: Achieving Fold Reduction
As the world continues to make strides in the fight against new and existing illnesses, scientists and pharmaceutical companies are turning to machine learning to help speed up and improve the drug discovery process.
Researchers from the University of Edinburgh have developed a method of using machine learning algorithms to identify the most efficient way to reduce the number of steps required in the drug discovery process. This method, called fold reduction, works by analyzing the structure of a particular drug molecule and identifying which steps in the drug design process are unnecessary. By eliminating these steps, the drug discovery process can be made much faster and more efficient.
This new technique is expected to revolutionize the way drugs are discovered, and could lead to faster and more effective treatments for a variety of illnesses. With the use of machine learning, drug discovery can be significantly streamlined and made more efficient, ultimately leading to improved treatments for patients around the world.
source: Phys.org