A high-precision machine learning model could help accelerate drug discovery, scientists have showed through a study published in Science Advances.
The new model can accurately predict the interactions between a protein and a drug molecule based on a handful of reference experiments or simulations. The model is so fast that it only requires a few training references and once trained, it can predict whether or not a candidate drug molecule will bind to a target protein with 99{c0986b2a9275d76b7a0ba973f056fa63aa4a0690ad4d1155b300910433c7dd97} accuracy.
This is equivalent to predicting with near-certainty the activity of hundreds of compounds after actually testing them – by running only a couple dozen tests. The new method could accelerate the screening of candidate molecules thousands of times over.
The algorithm can also tackle materials-science problems such as modelling the subtle properties of silicon surfaces, and promises to revolutionise materials and chemical modelling – giving insight into the nature of intermolecular forces.
The design of this 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.
The approach is remarkably successful in predicting the stability of organic molecules, as well as the subtle energy balance governing the silicon structures crucial for microelectronic applications, and does so at a tiny fraction of the computational effort involved in a quantum mechanical calculation.
The research illustrates how chemical and materials discovery is now benefitting from the Machine Learning and Artificial Intelligence approaches that already underlie technologies from self-driving cars to go-playing bots and automated medical diagnostics.
New algorithms allow us to predict the behaviour of new materials and molecules with great accuracy and little computational effort, saving time and money in the process.