A system of 'automated design' for new drugs could help develop the complex therapies needed for many medical conditions while also improving drug safety and efficiency, new research from the University of Dundee has shown.
The 'Moneyball' approach taken by the research team utilises the principles of advanced statistical and data analysis which have seen to be increasingly influential in areas as varied as sport, finance and in forecasting the recent US Presidential election.
As more complex drugs are needed to treat more complicated problems - particularly in areas such as neuroscience, infectious diseases and cancer - the task facing biologists and chemists is daunting. However, researchers at the College of Life Sciences at Dundee, in collaboration with partners in North America, have shown that an automated computational process analysing huge amounts of existing data could provide a valuable new tool in drug discovery.
The innovative approach taken by the research team mimics the creative process of human chemists, where drug molecules are steadily improved through successive cycles of design and selection.
"One of the things that makes drug discovery so hard is that you're trying to improve several different properties at the same time," said Professor Andrew Hopkins, Chair of Medicinal Informatics at Dundee. "Evolution is a mechanism than can be applied to solving these kinds of optimisation problems, and the iterative process of adaption and selection of hundreds of thousand of possible solutions can be simulated in a computer.
"We have effectively proved the concept of automated design of new compounds, showing that by using algorithms to process massive amounts of data we can tackle problems of huge complexity. The system solves the design problem by using computational evolution to mimic the design process of human chemists but running it on a very large scale."
The research is published in the j
|Contact: Roddy Isles|
University of Dundee