K supercomputer-based drug discovery project by Biogrid pharma consortium

Abstract

In drug discovery process, in-silico computational methods to efficiently explore and optimize drug candidates among huge amount of organic compounds are strongly required. In this study, we aim to construct a novel computational workflow on “K-computer” that overcomes fundamental problems inherent in calculation accuracy and computational cost. Also, the workflow is constructed reflecting assessments by reseachers in pharmaceutical companies because it should be easy to handle and useful in practical drug development process. Based on these concepts, we have implemented CGBVS (Chemical Genomics-based Virtual Screening method) and MP-CAFEE (Massively Parallel Computation of Absolute binding Free Energy method) on “K-computer”. CGBVS is a virtual screening method based on big data analysis and enabled ultrafast prediction of binding of 18,930,000,000 protein-compound pairs (631 kinds of kinases and GPCRs x 30,000,000 compounds). In contrast, because MP-CAFEE is based on molecular dynamics simulation including water molecules, the method successfully predicted the protein-compound binding free energy (dG) for five sets of inhibitors targeting CHK1, CDK2, ERK2 kinases, urokinase, and GPCR. In this talk, I will report activities of the project and their outcomes up to the present.