The most fundamental goal in drug design is to predict whether a given molecule will bind to a target and if so how strongly. Molecular mechanics or molecular dynamics are most often used to predict the conformation of the small molecule and to model conformational changes in the biological target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, polarizability, etc.) of the drug candidate that will influence binding affinity.

Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity. Also, knowledge-based scoring function may be used to provide binding affinity estimates. These methods use linear regression, machine learning, neural nets or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.

Ideally, the computational method will be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized, saving enormous time and cost. The reality is that present computational methods are imperfect and provide, at best, only qualitatively accurate estimates of affinity. In practice it still takes several iterations of design, synthesis, and testing before an optimal drug is discovered. Computational methods have accelerated discovery by reducing the number of iterations required and have often provided novel structures.

Drug design with the help of computers may be used at any of the following stages of drug discovery:

Øhit identification using virtual screening (structure- or ligand-based design)

Øhit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.)

Ølead optimization optimization of other pharmaceutical properties while maintaining affinity

The Milky Way II Supercomputer System

ØThe Milky Way II supercomputer system, developed by the National University of Defense Technology (NUDT), is the outstanding achievement of the National 863 Program.

ØWith 16,000 computer nodes, each comprising two Intel Ivy Bridge Xeon processors and three Xeon Phi coprocessor chips, it represents the world's largest installation of Ivy Bridge and Xeon Phi chips, counting a total of 3,120,000 cores. Each of the 16,000 nodes possess 88 gigabytes of memory (64 used by the Ivy Bridge processors, and 8 gigabytes for each of the Xeon Phi processors). The total CPU plus coprocessor memory is 1,375 TiB (approximately 1.34 PiB).

ØDuring the testing phase, the Milky Way II supercomputer system was laid out in a non-optimal confined space. When assembled at its final location, the system will have a theoretical peak performance of 54.9 petaflops. At peak power consumption, the system itself would draw 17.6 megawatts of power. Including external cooling, the system would draw an aggregate of 24 megawatts. The computer complex would occupy 720 square meters of space.

ØThe front-end system consists of 4096 Galaxy FT-1500 CPUs, a SPARC derivative designed and built by NUDT. Each FT-1500 has 16 cores and a 1.8 GHz clock frequency. The chip has a performance of 144 gigaflops and runs on 65 watts. The interconnect, called the TH Express-2, designed by NUDT, utilizes a fat tree topology with 13 switches each of 576 ports.