Machine Learning and Drug Discovery

theoretical and computational chemistry, artificial intelligence technologies

This project is a collaboration between Cambridge and Newcastle Universities and AstraZeneca (via the CASE programme), and will focus on the design of novel machine learning based force fields for use in computer-aided drug discovery.

Accurate computational methods for predicting the strength of binding between a candidate drug molecule and its therapeutic target have the potential to revolutionise the drug discovery process. Our goal is to improve the accuracy of this approach by combining free energy calculations with the Gaussian Approximation Potential (GAP), which employs machine learning techniques to faithfully reproduce the quantum mechanical potential energy surface of the drug molecule.

This project will be coordinated by two supervisors with expertise in machine learning
(Prof Gábor Csányi, University of Cambridge) and computer-aided drug design (Dr Daniel Cole, Newcastle University) aspects of this research. The student will work closely with drug discovery programmes at AstraZeneca (Dr Graeme Robb) with the goal of establishing these computational methods as part of the standard tool kit in the drug discovery pipeline.

The project will provide highly sought-after training in the fields of computational medicinal chemistry and machine learning.