Mitsugu Araki

2.4k citations

49 papers · 1.4k indexed · 1 hit paper · h-index 20

Molecular Biology top 10%
Pulmonary and Respiratory Medicine top 5%
Oncology top 10%
Materials Chemistry
Computational Theory and Mathematics top 5%

Co-authors: Yasushi Okuno Ryohei Katayama Naoya Fujita Ken Uchibori Atsuo Tamura Mayumi Kamada Naohiko Inase Shigeo Sato
Topics: Protein Structure and Dynamics (19 papers)Enzyme Structure and Function (15 papers)Lung Cancer Treatments and Mutations (11 papers)
Cited by: Molecular Biology Oncology Pulmonary and Respiratory Medicine
Journals: Nature Journal of the American Chemical Society Journal of Biological Chemistry
Partner nations: Japan United Kingdom Canada

In The Last Decade

Mitsugu Araki

44 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer

2017 329 citations Ken Uchibori, Naohiko Inase et al. Nature Communications profile →

Peers

Countries citing papers authored by Mitsugu Araki

Since Specialization

Citations

This map shows the geographic impact of Mitsugu Araki's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mitsugu Araki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitsugu Araki more than expected).

Fields of papers citing papers by Mitsugu Araki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mitsugu Araki. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mitsugu Araki. The network helps show where Mitsugu Araki may publish in the future.

Co-authorship network of co-authors of Mitsugu Araki

This figure shows the co-authorship network connecting the top 25 collaborators of Mitsugu Araki. A scholar is included among the top collaborators of Mitsugu Araki based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mitsugu Araki. Mitsugu Araki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation 2025 ·PNAS Nexus ·Shigeyuki Matsumoto,(unknown),Ryo Kanada,Biao Ma,Mitsugu Araki,Shuntaro Chiba,Atsushi Tokuhisa,Hiroaki Iwata,Shoichi Ishida,Yoshinobu Akinaga,Kei Terayama,Ryosuke Kojima,(unknown),Kazuhiro Takemura,Teruki Honma,Akio Kitao,Yasushi Okuno	0
2	A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer 2024 ·npj Precision Oncology ·Mai Suzuki,Ken Uchibori,Tomoko Oh‐hara,(unknown),(unknown),Ai Takemoto,Mitsugu Araki,Shigeyuki Matsumoto,(unknown),Mutsuko Kukimoto‐Niino,Yusuke Kawase,Mikako Shirouzu,Yasushi Okuno,Makoto Nishio,Naoya Fujita,Ryohei Katayama	5
3	LTK mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring CLIP1-LTK fusion 2024 ·Communications Biology ·(unknown),Hiroki Izumi,Mitsugu Araki,Jie Liu,(unknown),(unknown),(unknown),Biao Ma,(unknown),(unknown),Shogo Kumagai,(unknown),Kosuke Tanaka,Yuji Shibata,Hibiki Udagawa,Shingo Matsumoto,(unknown),Yasushi Okuno,Kōichi Goto,Susumu Kobayashi	1
4	Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL 2023 ·Communications Biology ·Gert‐Jan Bekker,Mitsugu Araki,(unknown),Yasushi Okuno,Narutoshi Kamiya	10
5	Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis 2022 ·Cancer Research ·Junya Tabata,Takashi Nakaoku,Mitsugu Araki,Ryunosuke Yoshino,Shinji Kohsaka,Ayaka Otsuka,Masachika Ikegami,Ayako Ui,Shin-ichiro Kanno,(unknown),Shigeyuki Matsumoto,(unknown),(unknown),Masakazu Sekijima,Hiroyuki Mano,Yasushi Okuno,Aikou Okamoto,Takashi Kohno	2
6	Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations 2021 ·Journal of Chemical Information and Modeling ·Biao Ma,Kei Terayama,Shigeyuki Matsumoto,(unknown),(unknown),Hiroaki Iwata,Mitsugu Araki,Yasushi Okuno	44
7	Microsecond-timescale MD simulation of EGFR minor mutation predicts the structural flexibility of EGFR kinase core that reflects EGFR inhibitor sensitivity 2021 ·npj Precision Oncology ·Takahiro Yoshizawa,Ken Uchibori,Mitsugu Araki,Shigeyuki Matsumoto,Biao Ma,Ryo Kanada,Yosuke Seto,Tomoko Oh‐hara,Sumie Koike,Ryo Ariyasu,Satoru Kitazono,Hironori Ninomiya,Kengo Takeuchi,Noriko Yanagitani,Satoshi Takagi,Kazuma Kishi,Naoya Fujita,Yasushi Okuno,Makoto Nishio,Ryohei Katayama	16
8	Extraction of protein dynamics information from cryo-EM maps using deep learning 2021 ·Nature Machine Intelligence ·Shigeyuki Matsumoto,Shoichi Ishida,Mitsugu Araki,Takayuki Kato,Kei Terayama,Yasushi Okuno	51
9	Oncogenic mutations Q61L and Q61H confer active form-like structural features to the inactive state (state 1) conformation of H-Ras protein 2021 ·Biochemical and Biophysical Research Communications ·Shigeyuki Matsumoto,(unknown),Mitsugu Araki,Takashi Kawamura,Ryo Miyamoto,(unknown),Fumi Shima,Takashi Kumasaka,Yasushi Okuno,Tohru Kataoka	5
10	Novel Resistance Mechanisms Including L1196Q, P1094H, and R1248_D1249 Insertion in Three Patients With NSCLC After ALK Tyrosine Kinase Inhibitor Treatment 2020 ·Journal of Thoracic Oncology ·(unknown),Mitsugu Araki,Katsuhiro Masago,(unknown),Shiro Fujita,(unknown),Junichi Shimizu,Yoshitsugu Horio,Eiichi Sasaki,Waki Hosoda,Ryohei Katayama,Yasushi Okuno,Toyoaki Hida	6
11	Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method 2020 ·Scientific Reports ·(unknown),Shuntaro Chiba,(unknown),Shigeyuki Matsumoto,Biao Ma,Ryohei Katayama,Mitsugu Araki,Yasushi Okuno	9
12	Landscape and function of multiple mutations within individual oncogenes 2020 ·Nature ·Yuki Saito,Junji Koya,Mitsugu Araki,Yasunori Kogure,Sumito Shingaki,Mariko Tabata,Marni B. McClure,Kota Yoshifuji,Shigeyuki Matsumoto,(unknown),Hiroko Tanaka,Takanori Kanai∥,Satoru Miyano,Yuichi Shiraishi,Yasushi Okuno,Keisuke Kataoka	84
13	Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach 2020 ·Bioorganic & Medicinal Chemistry Letters ·Mitsugu Araki,(unknown),Hiroaki Iwata,(unknown),Katsuyoshi Masuda,Yasushi Okuno	12
14	Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance 2019 ·EBioMedicine ·Koutaroh Okada,Mitsugu Araki,(unknown),Biao Ma,Ryo Kanada,Noriko Yanagitani,Atsushi Horiike,Sumie Koike,Tomoko Oh‐hara,Kana Watanabe,Keiichi Tamai,Makoto Maemondo,Makoto Nishio,Takeshi Ishikawa,Yasushi Okuno,Naoya Fujita,Ryohei Katayama	87
15	Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods 2018 ·ACS Chemical Neuroscience ·(unknown),Mitsugu Araki,Masashi Yoshimura,Narutoshi Kamiya,Masahiro Ono,Hideo Saji,Yasushi Okuno	15
16	Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancerbreakdown → 2017 ·Nature Communications ·Ken Uchibori,Naohiko Inase,Mitsugu Araki,Mayumi Kamada,Shigeo Sato,Yasushi Okuno,Naoya Fujita,Ryohei Katayama	329
17	Machine learning accelerates MD-based binding pose prediction between ligands and proteins 2017 ·Bioinformatics ·Kei Terayama,Hiroaki Iwata,Mitsugu Araki,Yasushi Okuno,Koji Tsuda	19
18	Crystal structures of the state 1 conformations of the GTP‐bound H‐Ras protein and its oncogenic G12V and Q61L mutants 2012 ·FEBS Letters ·Shin Muraoka,Fumi Shima,Mitsugu Araki,Tomoko Inoue,(unknown),Yuichi Ijiri,(unknown),Atsuo Tamura,Takashi Kumasaka,Masaki Yamamoto,Tohru Kataoka	63
19	Solution Structure of the State 1 Conformer of GTP-bound H-Ras Protein and Distinct Dynamic Properties between the State 1 and State 2 Conformers 2011 ·Journal of Biological Chemistry ·Mitsugu Araki,Fumi Shima,Yoko Yoshikawa,Shin Muraoka,Yuichi Ijiri,(unknown),(unknown),Tohru Kataoka,Atsuo Tamura	85
20	Transformation of an α‐helix peptide into a β‐hairpin induced by addition of a fragment results in creation of a coexisting state 2006 ·Proteins Structure Function and Bioinformatics ·Mitsugu Araki,Atsuo Tamura	21

About Mitsugu Araki

Mitsugu Araki is a scholar working on Structural Biology, Computational Theory and Mathematics and Molecular Biology, having authored 49 papers that have together received 1.4k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (19 papers), Enzyme Structure and Function (15 papers) and Lung Cancer Treatments and Mutations (11 papers). The work is most often cited by research in Molecular Biology (1.1k citations), Oncology (394 citations) and Pulmonary and Respiratory Medicine (426 citations). Mitsugu Araki has collaborated with scholars based in Japan, United Kingdom and Canada. Frequent co-authors include Yasushi Okuno, Ryohei Katayama, Naoya Fujita, Ken Uchibori, Atsuo Tamura, Mayumi Kamada, Naohiko Inase, Shigeo Sato, Narutoshi Kamiya and Tohru Kataoka. Their work appears in journals such as Nature, Journal of the American Chemical Society and Journal of Biological Chemistry.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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