2015 Fiscal Year Annual Research Report
Development of an intelligent dynamic docking pipeline for improving molecular docking simulations
| Project/Area Number |
26730152
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
Hsin KunYi 沖縄科学技術大学院大学, 統合オープンシステムユニット, 研究員 (60604155)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Keywords | Docking Simulation / Molecular Dynamics / Network Pharmacology / Machine Learning / Molecular Interaction / Drug Discovery |
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| Outline of Annual 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. It is mainly composed of a high-precision scoring function for molecular simulation with a well-designed machine learning model. This pipeline enables researchers to predictively screen a large number of small molecules over a complex molecular pathway, allowing comprehensively identifying the on-/off-targets. We have also developed a publicly accessible website sharing the screening facility to researchers, dedicating our achievements to the community of drug discovery.
For prediction validation, we tested our method using PDBbind dataset, containing about three thousand protein-ligand complexes. By assessing the correlations between the prediction scores and the experimental binding affinities, it shown a good performance in predicting the binding potentials. The correlations have been improved to >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, including Nucleic Acids Research, Scientific Reports and IEEE. 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.
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| Remarks |
We have been developing a web-based & open-type prediction system for investigating "systems pharmacology" of a given compound freely accessible
for the drug discovery community.
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[Journal Article] Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target.2015
Author(s)
Chiba, S., Ikeda, K., Ishida, T., Gromiha, M.M., Taguchi, Y., Iwadate, M., Umeyama, H., Hsin, K. Y., Kitano, H. and Yamamoto, K.
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Journal Title
Scientific Reports
Volume: 5
Pages: 1
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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