Elsevier

Drug Discovery Today

Volume 13, Issues 23–24, December 2008, Pages 1052-1058
Drug Discovery Today

Review
Informatics
The impact of accelerator processors for high-throughput molecular modeling and simulation

https://doi.org/10.1016/j.drudis.2008.08.001Get rights and content

The recent introduction of cost-effective accelerator processors (APs), such as the IBM Cell processor and Nvidia's graphics processing units (GPUs), represents an important technological innovation which promises to unleash the full potential of atomistic molecular modeling and simulation for the biotechnology industry. Present APs can deliver over an order of magnitude more floating-point operations per second (flops) than standard processors, broadly equivalent to a decade of Moore's law growth, and significantly reduce the cost of current atom-based molecular simulations. In conjunction with distributed and grid-computing solutions, accelerated molecular simulations may finally be used to extend current in silico protocols by the use of accurate thermodynamic calculations instead of approximate methods and simulate hundreds of protein–ligand complexes with full molecular specificity, a crucial requirement of in silico drug discovery workflows.

Section snippets

Accelerator processors

Historically, microprocessor performance has improved primarily through the raising of clock speeds, make possible by the development of ever-finer fabrication processes. In recent years, it has become increasingly difficult to keep increasing clock speeds because of fundamental limits in the process technology and power consumption. Performance has also been limited by the increasing relative cost of accessing main memory, the speed of which has increased at a slower rate than CPUs. Despite

Software re-engineering

Although APs offer high peak computational performance, exploiting this efficiently comes at the cost of ease of use: codes must be refactored as highly parallelized programs. Code redesign and redevelopment has a very high cost that, when weighted against the traditional ‘free’ performance increase provided by the next iteration of conventional hardware, has in the past significantly limited the appeal of special-purpose hardware on the high-performance computing market. In the next few years,

Accelerated modeling

Despite the very recent introduction of APs into the market, a variety of applications targeting different industrial and scientific fields have already appeared. Excellent performance speed-ups have been reported for such diverse cases as computational finance [22], fluid dynamics [23], sequence alignment [24] and quantum chemistry [25]. These notable achievements for such a variety of algorithms highlight the potential of APs for computational science in general.

Accelerators processors will

Distributed computing in the accelerated era

Computational grids enable scientists to distribute simulations across a pool of machines to benefit from their aggregate power, and have already proved useful for fast calculations of binding affinities using MD techniques [31]. Owing to the computational cost of molecular simulations, the grid is usually composed of very expensive parallel-processing HPC resources, the costs of which limit the applicability of the approach. When distributed computing is combined with AP-equipped hardware,

Future outlook for medium-throughput molecular modeling

There is great interest in methods for supporting and optimizing experimental high-throughput screening 4, 38 to identify, characterize and optimize possible leads for a given target out of the vast number of viable chemical compounds. It is, however, very difficult for such methods to account correctly for the many phenomena involved in complex formation, such as the subtle interplay between entropy and enthalpy, conformational changes of the ligand or the substrate, presence of water

Conclusion

APs have the potential to provide a radical change in scientific and industrial computation. With an effective performance tens of times that of standard computers and doubling each 8–12 months, unprecedented levels of computational power can be put into the hands of scientists and programmers at an affordable cost. Such disruptive technology is already being used by research groups and companies in many different fields, and applications targeting molecular modeling are appearing at a steady

Conflicts of interest statement

We notify the journal that the authors are scientific consultants and also share holders of Acellera Ltd, a UK-based company selling software solutions for accelerated processors.

Acknowledgements

We gratefully acknowledge support from Barcelona supercomputing center (http://www.bsc.es), Acellera Ltd (http://www.acellera.com), Sony Computer Entertainment Spain (http://www.scee.com) and Nvidia corporation (http://www.nvidia.com). We thank Jordi Mestres and Ferran Sanz for a critical reading of the manuscript. GG acknowledges the Aneurist project (http://www.aneurist.org) for financial support. GDF acknowledges support from the Ramon y Cajal scheme.

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