Machine learning algorithm could accelerate drug discovery

That is according to new research that shows the algorithm can predict with 99% accuracy whether or not a candidate drug molecule will bind to a target protein.

The researchers said that is equivalent to predicting with “near-certainty” the activity of hundreds of compounds after testing them. The new method, say the researchers, could accelerate the screening of candidate molecules “thousands of times over”.

The design of the algorithm, which combines local information from the neighbourhood of each atom in a structure, makes it applicable across many different classes of chemical, materials science, and biochemical problems.

As a result, it could also tackle materials science issues such as modelling the subtle properties of silicon surfaces, the researchers added.

James Kermode, from the University of Warwick's Warwick Centre for Predictive Modelling and the School of Engineering, said: "This work is exciting because it provides a general-purpose machine learning approach that is applicable both to materials and molecules.

"The research is expected to lead to a significant increase in the accuracy and transferability of models used for drug design and to describe the mechanical properties of materials."

A full account of the research has been published in the journal Science Advances.