Challenge 1: How can in-silico research benefit from deep learning or quantum computing?
Deep learning has led to spectacular advances in computing over the last years, conquering fields which previously seemed to be reserved for human intelligence and creativity – for example, mastering the game of Go, recognizing images, or imitating the painting style of an artist. How can similar algorithms be applied to predictm the properties and behaviour of new chemical substances or to suggest substances that would fit a given purpose?
In ‘traditional’ in-silico research the simulation of a molecule is a difficult task due to the quantum nature of its particles, which leads to a very large number of degrees of freedom for the system. In quantum computing all information is stored in Q-bits, which behave inherently in a quantum-mechanical way. Is it possible to exploit this analogy and create super-efficient algorithms to simulate molecules?