By Gerard Martinez-Rosell
ACEMD was introduced in the field of Molecular Dynamics (MD) back in 2008. Its release, together with other MD engines, revolutionized the simulation throughput by leveraging the intrinsic parallelism present in a specific computer processor termed GPU (Graphic Processor Unit). Using a cluster of GPUs or a distributed network like GPUGRID one is able to reach performances of HPC-class computers using commodity software, yielding a significant (x10) reduction in the Euro/simulated time cost. Furthermore, the evolution of GPUs performance, which is mainly driven by a market demanding more graphic processing power (e.g. in videogames), has doubled approximately every 3 years and with it, since 2008, ACEMD performance has increased 2.1x. To illustrate this fact, we have compiled the maximum simulated time per year published by prof. Gianni de Fabritiis using ACEMD (which yields the oldest historic data) and we extrapolate that the MD field will reach the second aggregated time timescale by 2022.
It is really interesting to envision how things will have changed by then and how the increased performance will help to overcome current challenges. For instance, two of the present issues in MD are polarizability and fix protonation. These limitations are a model simplification necessary to reduce the computational cost that could be overcome in exchange of more computational power. One could easily picture that, in a context of computer power abundance, polarizable force fields and constant-pH simulations will become much more popular and the main choices to address the study of systems especially sensitive to those effects such as certain ligand-protein binding complexes or pH-sensitive proteins. Furthermore, access to increased computational power will probably motivate the community to tackle currently impossible approaches such as mesoscale simulations (e.g. whole protein cascade pathway, full virus, etc.).
Analysis and Perspective
We have analyzed a large number of MD studies carried out since the release of ACEMD engine in 2008. One important lesson we can learn from these studies is that, while an initial phase of proof of concept and technique validation is necessary to evolve and mature MD simulations as any other technique, the field is getting closer to the inflexion point where both accuracy (i.e. force field improvement) and precision (i.e. higher throughput) together with the introduction of tools that decrease the learning curve and entry-point to the technology (e.g. HTMD software package) will allow MD to become a mainstream technique routinely used in drug discovery. This inflexion point will have several practical implications, including a shift of the current paradigm to a new one in which MD is not only used in retrospective and validation studies but into prospective searches where MD leverages its full predictive potential decipher the mechanism of unknown biological processes and bring new drugs into the market
M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009)
G Martinez-Rosell, T Giorgino, MJ Harvey, G de Fabritiis, Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale in Curr Top Med Chem. 2017 Apr 14.
S. Doerr, MJ. Harvey, F. Noé and G. De Fabritiis. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput. (2016) 12 (4), pp 1845-1852