Bioinformaticsin silico Drug Discovery
Development of platform for efficiency of drug discovery by Machine learning, Augmented Reality, and Supercomputing and its application to search for therapeutic agents for tropical diseases.
Efficient combination of drug discovery and IT, Nikkei Shimbun
The next generation of leaders, Nikkei Sangyo Shimbun
Business and university seriously cooperate, Nikkan Kogyo Shimbun
Research Scientist, AIST
Associate Professor, GSIC, Tokyo Institute of Technology
Unit Leader/Associate Professor, Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology
Associate Professor, School of Computing, Tokyo Institute of Technology
2014 Young Researcher’s Award 2014 by IIBMP
2017 SIGBIO Achievement Award 2017 by SIGBIO, IPSJ
S. Chiba et al, “Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target”, Scientific Reports, 5, doi:10.1038/srep17209 ,2015.
R. Yoshino et al., ” Pharmacophore Modeling for Anti-Chagas Drug Design Using the Fragment Molecular Orbital Method “, PLoS ONE, 10(5):e0125829, doi:10.1371/journal.pone.0125829, 2015.
S. Chiba et al., “An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes”, Scientific Reports, 7, doi:10.1038/s41598-017-10275-4, 2017.
R. Yoshino et al., “In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease”, Scientific Reports, 7, doi:10.1038/s41598-017-06411-9, 2017.
N. Wakui et al., “Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: a molecular dynamics simulation approach”, Journal of Molecular Graphics and Modelling, doi.org/10.1016/j.jmgm.2017.11.011, 2018.