Hideaki Mamada

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 11

Top Topics

Diffusion Weighted Imaging

Machine Learning

Top Venues

Journal of medicinal chemistry

ACS medicinal chemistry letters

Journal of chemical information and modeling

This page only lists publications with an associated author ORCID identifier.

Publications

Kazuki Otake, Yoshinori Hara, Minoru Ubukata, Masafumi Inoue, Noboru Nagahashi, Dai Motoda, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Correction to "Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors". Journal of medicinal chemistry (2024)
Kazuki Otake, Yoshinori Hara, Minoru Ubukata, Masafumi Inoue, Noboru Nagahashi, Dai Motoda, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors. Journal of medicinal chemistry 67 (5) (2024)
Kazuki Otake, Yoshinori Hara, Minoru Ubukata, Masafumi Inoue, Noboru Nagahashi, Dai Motoda, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors. Journal of medicinal chemistry 67 (5) (2024)
Kazuki Otake, Yoshinori Hara, Minoru Ubukata, Masafumi Inoue, Noboru Nagahashi, Dai Motoda, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors. Journal of medicinal chemistry (2024)
Kazuki Otake, Yoshinori Hara, Minoru Ubukata, Masafumi Inoue, Noboru Nagahashi, Dai Motoda, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors. Journal of medicinal chemistry 67 (5) (2024)
Hideaki Mamada, Mari Takahashi, Mizuki Ogino, Yukihiro Nomura, Yoshihiro Uesawa
Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening. ACS omega 8 (40) (2023)
Kazuki Otake, Minoru Ubukata, Noboru Nagahashi, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Methyl and Fluorine Effects in Novel Orally Bioavailable Keap1-Nrf2 PPI Inhibitor. ACS medicinal chemistry letters 14 (5) (2023)
Kazuki Otake, Minoru Ubukata, Noboru Nagahashi, Naoki Ogawa, Yoshiji Hantani, Rie Hantani, Tsuyoshi Adachi, Akihiro Nomura, Keishi Yamaguchi, Mariko Maekawa, Hideaki Mamada, Takahisa Motomura, Motohide Sato, Kazuhito Harada
Methyl and Fluorine Effects in Novel Orally Bioavailable Keap1-Nrf2 PPI Inhibitor. ACS medicinal chemistry letters 14 (5) (2023)
Hideaki Mamada, Yukihiro Nomura, Yoshihiro Uesawa
Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning. ACS omega 7 (20) (2022)
Hiroaki Iwata, Tatsuru Matsuo, Hideaki Mamada, Takahisa Motomura, Mayumi Matsushita, Takeshi Fujiwara, Kazuya Maeda, Koichi Handa
Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data. Journal of chemical information and modeling 62 (17) (2022)
Hideaki Mamada, Yukihiro Nomura, Yoshihiro Uesawa
Prediction Model of Clearance by a Novel Quantitative Structure-Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning. ACS omega 6 (36) (2021)