Drug discovery and development is a complex and time-consuming process that involves the identification, validation, and optimization of potential drug candidates. Today, due to the exponential growth of biomedical data and the complexity of drug targets, this process requires extensive experimentation, data analysis, and modeling using high-performance computing (HPC).

Accelerating the speed, predictiveness, and accuracy of drug research provides not just a competitive edge, but ultimately saves lives. Key design considerations in HPC infrastructure design can make a dramatic difference in performance and maximize your research endeavors.

Recently, Graphics Processing Units (GPUs) have emerged as a powerful tool to bolster HPC for drug discovery and development. GPUs can accelerate many of the computationally intensive tasks involved in drug discovery, such as molecular docking, molecular dynamics simulations, and machine learning algorithms. By leveraging the parallel processing power of GPUs, drug developers can speed up the time it takes to identify promising drug candidates and bring them to market.

GPU-Accelerated Drug Discovery Use Cases

Molecular docking is a widely used technique in drug discovery that involves predicting the binding of a small molecule drug to a target protein. This is a computationally intensive task that involves calculating the potential energy between the drug and the protein. GPUs can accelerate this process by using parallel processing to complete the calculations across many cores, allowing for much faster and more accurate predictions.

Molecular dynamics simulations are another important tool in drug discovery, used to study the behavior of proteins and their interactions with small molecule drugs. These simulations require significant computational resources and has historically taken days or even weeks to complete. GPUs can reduce the time required for these simulations by several orders of magnitude, enabling researchers to study more complex systems and generate more accurate results in a fraction of the time it used to take.

Machine learning algorithms are becoming increasingly important in drug discovery and development. These algorithms can be used to analyze large amounts of data, identify patterns, and predict the properties of potential drug candidates. Once again, GPUs can significantly speed up the training of these algorithms by parallel processing the calculations across multiple GPUs.

In addition to speeding up the drug discovery and development process, GPU-powered HPC can also improve the accuracy of predictions and reduce the cost of drug development. By enabling researchers to study more complex systems and generate more accurate results, GPU-powered HPC can help identify potential drug candidates that are more likely to succeed in clinical trials, thereby reducing the likelihood of costly failures and accelerating the time to market.

Small Workstation-sized Systems

Small workstation-sized systems are an ideal option for drug researchers who need high-performance computing resources without requiring a full-scale server. GPU-accelerated workstations offer a great balance between processing power and affordability.

The NVIDIA A100 Tensor Core GPU is a popular choice for drug discovery workloads. It delivers exceptional acceleration at every scale for a wide variety of computing challenges. The A100 can efficiently scale up to thousands of GPUs or, using multi-instance GPU technology, be partitioned into seven isolated GPU instances to expedite workloads of all sizes. Its Tensor Core technology accelerates more levels of precision for diverse workloads, speeding time to insight as well as time to market.

Larger Server Clusters

For larger-scale drug discovery workloads, server clusters are a smart choice. Server clusters consists of multiple servers connected by a high-speed network, enabling users to run computationally intensive tasks on a much larger scale than is possible with a single workstation or server. They’re designed to handle large-scale workloads that require massive amounts of memory and storage.

With high computational power and memory, server clusters allow users to perform simulations on a much larger scale. They also provide increased scalability, enabling users to increase the number of servers in the cluster as their computing needs grow.

The NVIDIA H100 Tensor Core GPU is ideal for use in server clusters. It enables an order-of-magnitude leap for large-scale drug discovery HPC with unprecedented performance, scalability, and security. With the NVLink system allowing direct communication between up to 256 GPUs, the H100 accelerates every task up to exascale size workloads. Systems with NVIDIA H100 GPUs support PCIe Gen5, gaining 128GB/s of bi-directional throughput, and HBM3 memory, which provides 3TB/sec of memory bandwidth, eliminating bottlenecks for memory and network-constrained workflows.

More Efficient Drug Discovery and Development

GPU-accelerated HPC has the potential to revolutionize the drug discovery and development process. By accelerating computationally intensive tasks, researchers can identify promising drug candidates faster and more accurately than ever before. This can lead to significant cost savings and faster time to market for new drugs, ultimately improving patient outcomes and providing a huge competitive edge to those who implement their infrastructure the right way.

Silicon Mechanics offers customized GPU servers designed to meet your specific drug research needs. Our servers are built using high-quality components and are optimized for performance and efficiency, making them an ideal solution for CFD simulations. We offer a range of GPU-accelerated servers, from small workstation-sized systems to large-scale server clusters.

Learn more about how we can help you accelerate your CFD simulations and view our wide range of custom GPU servers at www.siliconmechanics.com/industries/sciences.