平野 秀典 (ヒラノ ヨシノリ)

Hirano, Yoshinori

写真a

所属(所属キャンパス)

理工学研究科 (矢上)

職名

特任准教授(有期)

学歴 【 表示 / 非表示

  • 1997年03月

    明治薬科大学, 薬学部, 製薬学科

    大学, 卒業

  • 2002年03月

    千葉大学, 薬学研究科, 総合薬品科学専攻

    大学院, 修了, 博士

 

研究分野 【 表示 / 非表示

  • 物理系薬学 (Physical Pharmaceutical Science)

 

論文 【 表示 / 非表示

  • Use of the Multilayer Fragment Molecular Orbital Method to Predict the Rank Order of Protein-Ligand Binding Affinities: A Case Study Using Tankyrase 2 Inhibitors

    Okimoto N., Otsuka T., Hirano Y., Taiji M.

    ACS Omega (ACS Omega)  3 ( 4 ) 4475 - 4485 2018年04月

     概要を見る

    In computational drug discovery, ranking a series of compound analogues in the order that is consistent with the experimental binding affinities remains a challenge. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics (MM)-based force fields, although they cannot completely describe protein-ligand interactions. By contrast, quantum mechanics (QM) calculations play an important role in understanding the protein-ligand interactions; however, their huge computational costs hinder their application in drug discovery. In this study, we have evaluated the ability to rank the binding affinities of tankyrase 2 ligands by combining both MM and QM calculations. Our computational approach uses the protein-ligand binding energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method combined with the solvation energy obtained from the MM-Poisson-Boltzmann/surface area (MM-PB/SA) method to predict the binding affinity. This approach enabled us to rank tankyrase 2 inhibitor analogues, outperforming several MM-based methods, including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that this computational approach using the MFMO method is a promising tool for predicting the rank order of the binding affinities of inhibitor analogues.

KOARA(リポジトリ)収録論文等 【 表示 / 非表示

 

担当授業科目 【 表示 / 非表示

  • 看護のための薬理学

    2021年度

  • 看護のための薬理学

    2019年度