Name
Abstract | ||
|---|---|---|
The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. The rapid and accurate quantification of the strength of these interactions (as measured by u0027binding affinityu0027) is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient specific medicine. Recent evidence suggests that molecular dynamics (MD) can achieve useful predictive accuracy (? 1 kcal/mol). For this predictive accuracy to impact clinical decision making, binding free energy results must be turned around rapidly and without loss of accuracy. This demands advances in algorithms, scalable software systems, and efficient utilization of supercomputing resources. We introduce a framework called HTBAC, designed to support accurate and scalable drug binding affinity calculations, while marshaling large simulation campaigns. We show that HTBAC supports the specification and execution of adaptive free-energy protocols at scale and with minimal overheads on NCSA Blue Waters. We validate the results obtained and show how adaptivity can be used to improve accuracy while reducing resource consumption of TIES, a widely used free-energy protocol. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Distributed, Parallel, and Cluster Computing | Calculator,Computer science,A priori and a posteriori,Invariant (mathematics),Scaling,Workflow,Blue Waters,Distributed computing,Scalability,Computation |
DocType | Volume | Citations |
Journal | abs/1801.01174 | 0 |
PageRank | References | Authors |
0.34 | 8 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jumana Dakka | 1 | 1 | 0.69 |
| Kristof Farkas-Pall | 2 | 0 | 0.68 |
| Vivek Balasubramanian | 3 | 0 | 0.68 |
| Matteo Turilli | 4 | 84 | 16.21 |
| David W. Wright | 5 | 18 | 7.35 |
| Shunzhou Wan | 6 | 8 | 4.02 |
| Stefan J. Zasada | 7 | 129 | 12.79 |
| Peter V. Coveney | 8 | 206 | 32.82 |
| Shantenu Jha | 9 | 188 | 32.40 |