Development of an intelligent dynamic docking pipeline for improving molecular docking simulations.

2015 Fiscal Year Final Research Report

• Project/Area Number: 26730152
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Life / Health / Medical informatics
• Research Institution: Okinawa Institute of Science and Technology Graduate University
• Principal Investigator: Hsin Kun-Yi 沖縄科学技術大学院大学, その他の研究科, 研究員 (60604155)
• Project Period (FY): 2014-04-01 – 2016-03-31
• Keywords: Docking Simulation / Molecular Dynamics / Network Pharmacology / Machine Learning / Molecular Interaction / Drug Discovery

Outline of Final Research Achievements

In order to precisely and efficiently predict the binding potentials of test compounds against proteins involved in a molecular pathway, we have developed a network pharmacology-based prediction pipeline. By assessing the correlations between the prediction scores and the experimental binding affinities, our prediction method shown a good performance in predicting the binding potentials (R >0.8).
Additionally, we predicted the selectivity of various kinase inhibitors by comparing with known bioassay results, showing a good consistency. The relevant research results have been published on high-impact journals. We have also applied it to several joined projects helping collaborators, including those in Systems Biology Institute (SBI, Tokyo) and The University of Tokyo (IMSUT), to identify druggable molecules. A publicly accessible website called “systemsDock” (http://systemsdock.unit.oist.jp/) has been published, dedicating our achievements to the community of drug discovery.

Free Research Field

Bioinformatics