- 著者
- Hirotomo Moriwaki Yu-Shi Tian Norihito Kawashita Tatsuya Takagi
- 出版者
- The Pharmaceutical Society of Japan
- 雑誌
- Chemical and Pharmaceutical Bulletin (ISSN:00092363)
- 巻号頁・発行日
- vol.67, no.5, pp.426-432, 2019-05-01 (Released:2019-05-01)
- 参考文献数
- 22
- 被引用文献数
- 3
Quantitative structure–activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related research domains. However, the requirement for accurate alignment of compounds in CoMFA increases the difficulties encountered in its use. This has led to the development of several techniques—such as VolSurf, Grid-independent descriptors (GRIND), and Anchor-GRIND—which do not require such an alignment. We propose a technique to construct the prediction model that uses molecular interaction field grid potentials as inputs to convolutional neural network. The proposed model has been found to demonstrate higher accuracy compared to the conventional descriptor-based QSAR models as well as Anchor-GRIND techniques. In addition, the method is target independent, and is capable of providing useful information regarding the importance of individual atoms constituting the compounds contained in the chemical dataset used in the proposed analysis. In view of these advantages, the proposed technique is expected to find wide applications in future drug-design operations.
- 著者
- Yusuke Kawashima Natsumi Mori Norihito Kawashita Yu-Shi Tian Tatsuya Takagi
- 出版者
- Chem-Bio Informatics Society
- 雑誌
- Chem-Bio Informatics Journal (ISSN:13476297)
- 巻号頁・発行日
- vol.21, pp.1-10, 2021-01-29 (Released:2021-01-29)
- 参考文献数
- 16
- 被引用文献数
- 1
Fragment molecular orbital (FMO) calculation is a useful ab initio method for analyzing protein–ligand interactions in the current structure-based drug design. When multiple ligands exist for one receptor, a post-FMO calculation tool is required because of large numbers of interaction energy decomposition terms calculated using this method. In this study, a method that combines self-organizing maps (SOM) and hierarchical clustering analysis (HCA) was proposed to analyze the results of the FMO energy components. This method could effectively compress the high-dimensional energy terms and is expected to be useful to analyze the interaction between protein and ligands. A case study of antitype 2 diabetes mellitus target DPP-IV and its inhibitors was analyzed to verify the feasibility of the proposed method. After performing dimensional compression using SOM and further grouping using HCA, we obtained superclasses of the inhibitors based on the dispersion energy (DI), which showed consistency with structural information, indicating that further analyses of detailed energies per superclass can be an effective approach for obtaining important ligand–protein interactions.
- 著者
- Yu-Shi Tian Yi Zhou Tatsuya Takagi Masanori Kameoka Norihito Kawashita
- 出版者
- The Pharmaceutical Society of Japan
- 雑誌
- Chemical and Pharmaceutical Bulletin (ISSN:00092363)
- 巻号頁・発行日
- vol.66, no.3, pp.191-206, 2018-03-01 (Released:2018-03-01)
- 参考文献数
- 103
- 被引用文献数
- 36
The global occurrence of viral infectious diseases poses a significant threat to human health. Dengue virus (DENV) infection is one of the most noteworthy of these infections. According to a WHO survey, approximately 400 million people are infected annually; symptoms deteriorate in approximately one percent of cases. Numerous foundational and clinical investigations on viral epidemiology, structure and function analysis, infection source and route, therapeutic targets, vaccines, and therapeutic drugs have been conducted by both academic and industrial researchers. At present, CYD-TDV or Dengvaxia® is the only approved vaccine, but potent inhibitors are currently under development. In this review, an overview of the viral life circle and the history of DENVs is presented, and the most recently reported antiviral candidates and newly discovered promising targets are focused and summarized. We believe that these successes and failures have enabled progress in anti-DENV drug discovery and hope that our review will stimulate further innovation in this area.