Quantum physics AI speeds up drug discovery
9 Dec 2019
An international collaboration has seen a team using Artificial Intelligence normally used to predict molecular wave functions and the electronic properties of molecules to speed-up the design of drug molecules or new materials.
An interdisciplinary team of chemists, physicists, and computer scientists led by the University of Warwick, and including the Technical University of Berlin, and the University of Luxembourg have developed a deep machine learning algorithm that can predict the quantum states of molecules, so-called wave functions, which determine all properties of molecules.
Solving the fundamental equations of quantum mechanics in the conventional way requires massive high-performance computing resources and months of computing time. This is typically the bottleneck to the computational design of new purpose-built molecules for medical and industrial applications. The team say the newly developed AI algorithm can supply accurate predictions within seconds on a laptop or mobile phone.
Dr Reinhard Maurer from the Department of Chemistry at the University of Warwick said: “This has been a joint three year effort, which required computer science know-how to develop an artificial intelligence algorithm flexible enough to capture the shape and behaviour of wave functions, but also chemistry and physics know-how to process and represent quantum chemical data in a form that is manageable for the algorithm.”
The team have been brought together during an interdisciplinary 3-month fellowship program at IPAM (UCLA) on the subject of machine learning in quantum physics.
Prof Dr Klaus Robert-Muller from the Institute of Software Engineering and Theoretical Computer Science at the Technical University of Berlin said: “This interdisciplinary work is an important progress as it shows that, AI methods can efficiently perform the most difficult aspects of quantum molecular simulations. Within the next few years, AI methods will establish themselves as essential part of the discovery process in computational chemistry and molecular physics.”
The work is published in Nature Communications.
