Laboratory for Structure-Based Molecular Design
- E-mail：honma.teruki[at]riken.jpPlease replace [at] with @.
New in silico molecular design technologies are developed and applied to drug discovery targets
Through the long history of small molecule drug discovery, conventional “druggable” targets had been already investigated and many drugs were marketed. On the other hand, in addition to refractory cancer, Alzheimer's disease, genetic diseases, the risk of new infections is increasing, so there are strong needs for innovative new drugs that have never existed. The team develops new technologies for in silico design and drug discovery artificial intelligence (AI) by combining simulation such as molecular dynamics and quantum chemical calculation (FMO method) and informatics technology represented by AI. The developed technologies are applied to in silico screening for drug discovery targets with high difficulty by conventional. Using the hits obtained by the screening, simultaneous optimization of multiple items necessary for medicines such as potency, pharmacokinetics, toxicity etc. is carried out. In addition, we develop and operate the world's first quantum chemistry calculation database (FMO IFIE database) of proteins.
New in silico drug discovery technologies.
PALLAS: a system for docking condition optimization
MUSES: affinity prediction using interaction descriptors
FMO-PBSA: affinity prediction based on QM and solvent effects
LAILAPS: a system for muti-directional ligand searching
the world's first quantum chemistry calculation database (FMO IFIE database) of proteins
- Development of new technologies for in silico drug discovery combining simulation and informatics
- Application of in silico drug discovery technologies to drug discovery targets
- Construction and publication of FMO IFIE database
Main Publications List
- Komura H, Watanabe R, Kawashima H, et al.
A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties.
Drug Discovery Today (2021) doi: 10.1016/j.drudis.2021.01.024
- Sato T, Hashimoto N, Honma T.
Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments.
Journal of Chemical Information and Modeling 57(12). 2938-2947 (2017) doi: 10.1021/acs.jcim.7b00092
- Watanabe C, Watanabe H, Fukuzawa K, et al.
Theoretical Analysis of Activity Cliffs among Benzofuranone Class Pim1 Inhibitors Using the Fragment Molecular Orbital Method with Molecular Mechanics Poisson-Boltzmann Surface Area (FMO+MM-PBSA) Approach.
Journal of Chemical Information and Modeling 57(12). 2996-3010 (2017) doi: 10.1021/acs.jcim.7b00110
- Okada-Iwabu M., Yamauchi T., Iwabu M., et al.
A small-molecule AdipoR agonist for type 2 diabetes and short life in obesity.
Nature 503. 493-499 (2013) doi: 10.1038/nature12656
- Saito Y., Yuki H., Kuratani M., et al.
A pyrrolo-pyrimidine derivative targets human primary AML stem cells in Vivo.
Science Translational Medicine 5(181). 181ra152 (2013) doi: 10.1126/scitranslmed.3004387
- Shiba T., Kido Y., Sakamoto K., et al.
Structure of the trypanosome cyanide-insensitive alternative oxidase.
Proceedings of the National Academy of Sciences of the United States of America 110(12). 4580-5 (2013) doi: 10.1073/pnas.1218386110
- Takaya D., Sato T., Yuki H., et al.
Prediction of Ligand-Induced Structural Polymorphism of Receptor Interaction Sites Using Machine Learning.
Journal of Chemical Information and Modeling 53 (3). 704–716 (2013) doi: 10.1021/ci300458g
- Sato T., Watanabe H., Tsuganezawa K., et al.
Identification of novel drug-resistant EGFR mutant inhibitors by in silico screening using comprehensive assessments of protein structures.
Bioorganic & Medicinal Chemistry 20(12). 3756-67 (2012) doi: 10.1016/j.bmc.2012.04.042
- Yuki H., Honma T., Hata M., Hoshino T.
Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4.
Bioorganic & Medicinal Chemistry 20(2). 775-83 (2011) doi: 10.1016/j.bmc.2011.12.004
- Sato T., Honma T., Yokoyama S.
Combining Machine Learning and Pharmacophore-based Interaction Fingerprint for in silico Screening.
Journal of Chemical Information and Modeling 50(1). 170-85 (2010) doi: 10.1021/ci900382e
- Sato T., Matsuo Y., Honma T., Yokoyama S.
In silico functional profiling of small molecules and its applications.
Journal of Medicinal Chemistry 51(24). 7705-16 (2008) doi: 10.1021/jm800504q
|Teruki HonmaTeam Leader||honma.teruki[at]riken.jp|
|Tomohiro SatoResearch Scientist||tomohiro.sato[at]riken.jp|
|Daisuke TakayaResearch Scientist||daisuke.takaya[at]riken.jp|
|Hitomi YukiResearch Scientist||hitomi.yuki[at]riken.jp|
|Chiduru WatanabeResearch Scientist||chiduru.watanabe[at]riken.jp|
|Mayuko YasudaTechnical Scientist||mayuko.yasuda[at]riken.jp|
|Shunpei NagaseTechnical Staff I||shunpei.nagase[at]riken.jp|
*：concurrent / Please replace [at] with @.