  Classical molecular dynamics (MD) simulations will be able to reach sampling
in the second timescale within five years, producing petabytes of simulation
data at current force field accuracy. Notwithstanding this, MD will still be in
the regime of low-throughput, high-latency predictions with average accuracy.
We envisage that machine learning (ML) will be able to solve both the accuracy
and time-to-prediction problem by learning predictive models using expensive
simulation data. The synergies between classical, quantum simulations and ML
methods, such as artificial neural networks, have the potential to drastically
reshape the way we make predictions in computational structural biology and
drug discovery.
