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.

The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from MINECO (BIO2017-82628-P) and FEDER, as well as ‘Unidad de Excelencia María de Maeztu’, funded by MINECO (MDM-2014-0370). The authors thank the European Union's Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project).