Hiroaki Iwata

1.3k total citations
38 papers, 796 citations indexed

About

Hiroaki Iwata is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Hiroaki Iwata has authored 38 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 21 papers in Computational Theory and Mathematics and 5 papers in Pharmacology. Recurrent topics in Hiroaki Iwata's work include Computational Drug Discovery Methods (21 papers), Bioinformatics and Genomic Networks (8 papers) and Protein Structure and Dynamics (7 papers). Hiroaki Iwata is often cited by papers focused on Computational Drug Discovery Methods (21 papers), Bioinformatics and Genomic Networks (8 papers) and Protein Structure and Dynamics (7 papers). Hiroaki Iwata collaborates with scholars based in Japan, United States and Russia. Hiroaki Iwata's co-authors include Osamu Gotoh, Yoshihiro Yamanishi, Yasushi Okuno, Ryusuke Sawada, Sayaka Mizutani, Ryosuke Kojima, Masaaki Kotera, Mitsugu Araki, Teruki Honma and Masateru Ohta and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Scientific Reports.

In The Last Decade

Hiroaki Iwata

34 papers receiving 782 citations

Peers

Hiroaki Iwata

CTM

PHARM

GENET

David Hoksza Czechia

Jiayi Yin China

Konda Mani Saravanan India

Wenhua Kuang China

Qifeng Bai China

Joel P. Arrais Portugal

Suzanne Brewerton United Kingdom

Nicole Redaschi Switzerland

Lisa Jeske Germany

Antje Chang Germany

David Hoksza Czechia

Hiroaki Iwata

811 ×1.8

407 ×1.2

CTM

125 ×1.5

50 ×0.7

PHARM

80 ×1.2

GENET

Citations per year, relative to Hiroaki Iwata Hiroaki Iwata (= 1×) peers David Hoksza

Countries citing papers authored by Hiroaki Iwata

Since Specialization

Citations

This map shows the geographic impact of Hiroaki Iwata'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 Hiroaki Iwata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hiroaki Iwata more than expected).

Fields of papers citing papers by Hiroaki Iwata

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hiroaki Iwata. 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 Hiroaki Iwata. The network helps show where Hiroaki Iwata may publish in the future.

Co-authorship network of co-authors of Hiroaki Iwata

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroaki Iwata. A scholar is included among the top collaborators of Hiroaki Iwata 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 Hiroaki Iwata. Hiroaki Iwata 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

Matsumoto, Shigeyuki, Ryo Kanada, Biao Ma, et al.. (2025). Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation. PNAS Nexus. 4(3). pgaf094–pgaf094.

Iwata, Hiroaki. (2025). AI-driven prediction of bitterness and sweetness and analysis of receptor interactions. Current Research in Food Science. 10. 101090–101090. 1 indexed citations

Iwata, Hiroaki, et al.. (2025). Lichen planus pigmentosus inversus presenting with clinical features mimicking acanthosis nigricans. Dermatology Online Journal. 30(6).

Iwata, Hiroaki, et al.. (2025). Image-based estimation of component ratios in pharmaceutical powders using optical microscopy and deep learning. Journal of Drug Delivery Science and Technology. 115. 107682–107682.

Iwata, Hiroaki, et al.. (2024). Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model. CPT Pharmacometrics & Systems Pharmacology. 14(3). 486–494. 3 indexed citations

Katayama, Sho, et al.. (2024). Clinical characteristics and outcomes of dipeptidyl peptidase-4 inhibitor-associated bullous pemphigoid patients: A retrospective study. Journal of the American Academy of Dermatology. 92(3). 561–564. 2 indexed citations

Igarashi, Yoshinobu, Ryosuke Kojima, Shigeyuki Matsumoto, et al.. (2024). Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method. The Journal of Toxicological Sciences. 49(3). 117–126. 3 indexed citations

Iwata, Hiroaki, et al.. (2023). VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search. Journal of Chemical Information and Modeling. 63(23). 7392–7400. 11 indexed citations

Iwata, Hiroaki. (2023). Application of <i>in Silico</i> Technologies for Drug Target Discovery and Pharmacokinetic Analysis. Chemical and Pharmaceutical Bulletin. 71(6). 398–405. 9 indexed citations

10.

Miyauchi, T., et al.. (2022). Case report: Difference in outcomes between two cases of Hailey-Hailey disease treated with apremilast. Frontiers in Genetics. 13. 884359–884359. 5 indexed citations

11.

Ma, Biao, Kei Terayama, Shigeyuki Matsumoto, et al.. (2021). Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. Journal of Chemical Information and Modeling. 61(7). 3304–3313. 44 indexed citations

12.

Iwata, Hiroaki, et al.. (2021). Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning. Journal of Pharmaceutical Sciences. 110(4). 1834–1841. 23 indexed citations

13.

Araki, Mitsugu, et al.. (2020). Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach. Bioorganic & Medicinal Chemistry Letters. 30(11). 127142–127142. 12 indexed citations

14.

Araki, Mitsugu, et al.. (2020). Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides. Heliyon. 6(6). e04227–e04227. 14 indexed citations

15.

Nishie, Wataru, et al.. (2018). 調節性T細胞機能障害はマウスおよびヒト被験者における水疱性類天疱瘡抗原に対する自己抗体を誘導する【JST・京大機械翻訳】. Journal of Allergy and Clinical Immunology. 142(6). 1818–1830. 17 indexed citations

16.

Terayama, Kei, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, & Koji Tsuda. (2017). Machine learning accelerates MD-based binding pose prediction between ligands and proteins. Bioinformatics. 34(5). 770–778. 19 indexed citations

17.

Iwata, M, Ryusuke Sawada, Hiroaki Iwata, Masaaki Kotera, & Yoshihiro Yamanishi. (2017). Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Scientific Reports. 7(1). 40164–40164. 40 indexed citations

18.

Sakamaki, Kazuhiro, Naoyuki Iwabe, Hiroaki Iwata, et al.. (2015). Conservation of structure and function in vertebrate c-FLIP proteins despite rapid evolutionary change. Biochemistry and Biophysics Reports. 3. 175–189. 7 indexed citations

19.

Sakamaki, Kazuhiro, Kouhei Shimizu, Hiroaki Iwata, et al.. (2014). The Apoptotic Initiator Caspase-8: Its Functional Ubiquity and Genetic Diversity during Animal Evolution. Molecular Biology and Evolution. 31(12). 3282–3301. 26 indexed citations

20.

Iwata, Hiroaki & Osamu Gotoh. (2011). Comparative analysis of information contents relevant to recognition of introns in many species. BMC Genomics. 12(1). 45–45. 33 indexed citations

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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