Accurate Scoring of Drug Conformations at the Extreme Scale

—We present a scalable method to extensively search for and accurately select pharmaceutical drug candidates in large spaces of drug conformations computationally generated and stored across the nodes of a large distributed system. For each ligand conformation in the dataset, our method first extracts relevant geometrical properties and transforms the properties into a single metadata point in the three-dimensional space. Then, it performs an octree-based clustering on the metadata to search for predominant clusters. Our method avoids the need to move ligand conformations among nodes because it extracts relevant data properties locally and concurrently. By doing so, we can perform accurate and scalable distributed clustering analysis on large distributed datasets. We scale the analysis of our pharmaceutical datasets a factor of 400X higher in performance and 500X larger in size than ever before. We also show that our clustering achieves higher accuracy compared with that of traditio...

• Type: Journal
• Year: 2015
• Where: CCGRID
• Keywords: CCGRID 2015, Distributed And Parallel Computing