担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.csbj.2025.01.022

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

Accurate prediction of the difference in binding free energy between compounds is crucial for reducing the high costs associated with drug discovery. Relative binding free energy perturbation (RBFEP) calculations are effective for small structural changes; however, large topological changes pose significant challenges for calculations, leading to high errors and difficulties in convergence. To address such issues, we propose a new approach─PairMap─that focuses on introducing appropriate intermediates for complex transformations between two input compounds. PairMap-generated intermediates exhaustively, determined the optimal conversion paths, and introduced thermodynamic cycles into the perturbation map to improve accuracy and reduce computational cost. PairMap succeeded in introducing appropriate intermediates that could not be discovered by existing simple approaches by comprehensively considering intermediates. Furthermore, we evaluated the accuracy of the prediction of binding free energy using 9 compounds selected from Wang et al.'s benchmark set, which included particularly complex transformations. The perturbation map generated by PairMap achieved excellent accuracy with a mean absolute error of 0.93 kcal/mol compared to 1.70 kcal/mol when using the perturbation map generated by the conventional Flare FEP intermediate introduction method. Moreover, in a scaffold hopping experiment conducted with the PDE5a target involving complex transformations, PairMap provided more accurate free energy predictions than ABFEP calculations, yielding more reliable results compared to experimental data. Additionally, PairMap can be utilized to introduce intermediates into congeneric series, demonstrating that complex links on the perturbation map can be resolved with minimal addition of intermediates and links. In conclusion, PairMap overcomes the limitations of existing methods by enabling RBFEP calculations for more complex transformations, further streamlining lead optimization in drug discovery.

DOI： 10.1021/acs.jcim.4c01634

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担当区分：最終著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11227-024-06860-w

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担当区分：筆頭著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11227-024-06861-9

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担当区分：最終著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.48550/arXiv.2409.10964

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担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BIBM62325.2024.10822009

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その他リンク： https://dblp.uni-trier.de/db/conf/bibm/bibm2024.html#TsutaokaKNLO24

担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/BIBM62325.2024.10822666

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その他リンク： https://dblp.uni-trier.de/db/conf/bibm/bibm2024.html#NishinoKTLO24

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3673038.3673097

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その他リンク： https://dblp.uni-trier.de/rec/conf/icpp/2024

担当区分：筆頭著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11227-024-06007-x

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担当区分：最終著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11227-024-06006-y

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担当区分：最終著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11227-023-05887-9

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その他リンク： https://dblp.uni-trier.de/db/journals/tjs/tjs80.html#UekiO24

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.

DOI： 10.1038/s42004-024-01155-w

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: https://github.com/ohuelab/SpatialPPI.

DOI： 10.1016/j.csbj.2024.03.009

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

Abstract

The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.

DOI： 10.1038/s42004-023-01054-6

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その他リンク： https://www.nature.com/articles/s42004-023-01054-6

担当区分：筆頭著者,　最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

MOTIVATION: Since the advent of ColabFold, numerous software packages have been provided with Google Colaboratory-compatible ipynb files, allowing users to effortlessly test and reproduce results without the need for local installation or configuration. MEGADOCK, a protein-protein docking tool, is particularly well-suited for Google Colaboratory due to its lightweight computations and GPU acceleration capabilities. To increase accessibility and promote widespread use, it is crucial to provide a computing environment compatible with Google Colaboratory. RESULTS: In this study, we report the development of a Google Colaboratory environment for running our protein-protein docking software, MEGADOCK. We provide a comprehensive ipynb file, including the compilation of MEGADOCK with the FFTW library installation on Colaboratory, the introduction of related tools using PyPI/apt, and the execution and visualization of docking structures. This streamlined environment enables users to visualize docking structures with just one click. The code is available under a CC-BY NC 4.0 license from https://github.com/ohuelab/MEGADOCK-on-Colab .

DOI： 10.1186/s13104-023-06505-w

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担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/CIBCB56990.2023.10264879

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その他リンク： https://dblp.uni-trier.de/db/conf/cibcb/cibcb2023.html#KengkannaO23

掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/ijms241713257

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.

DOI： 10.3390/molecules28155652

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Recently, cyclic peptides have been considered breakthrough drugs because they can interact with "undruggable" targets such as intracellular protein-protein interactions. Membrane permeability is an essential indicator of oral bioavailability and intracellular targeting, and the development of membrane-permeable peptides is a bottleneck in cyclic peptide drug discovery. Although many experimental data on membrane permeability of cyclic peptides have been reported, a comprehensive database is not yet available. A comprehensive membrane permeability database is essential for developing computational methods for cyclic peptide drug design. In this study, we constructed CycPeptMPDB, the first web-accessible database of cyclic peptide membrane permeability. We collected information on a total of 7334 cyclic peptides, including the structure and experimentally measured membrane permeability, from 45 published papers and 2 patents from pharmaceutical companies. To unambiguously represent cyclic peptides larger than small molecules, we used the hierarchical editing language for macromolecules notation to generate a uniform sequence representation of peptides. In addition to data storage, CycPeptMPDB provides several supporting functions such as online data visualization, data analysis, and downloading. CycPeptMPDB is expected to be a valuable platform to support membrane permeability research on cyclic peptides. CycPeptMPDB can be freely accessed at http://cycpeptmpdb.com.

DOI： 10.1021/acs.jcim.2c01573

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担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/CSCE60160.2023.00349

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担当区分：最終著者   記述言語：英語  

DOI： 10.3389/fmolb.2023.1190855

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担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/CSCE60160.2023.00350

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.

DOI： 10.1021/acs.jcim.2c00931

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing k-best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.

DOI： 10.1021/acsomega.2c03470

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

New protein-protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the "binder hallucination" protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.

DOI： 10.3390/biomedicines10071626

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

MOTIVATION: In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available. RESULTS: Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instead of those expressing the entire molecule, as well as the circularity of the cyclic peptides should be considered. Therefore, we developed a prediction method named CycPeptPPB using deep learning that considers both factors. First, the macrocycle ring of cyclic peptides was decomposed residue by residue. The residue-based descriptors were arranged according to the sequence information of the cyclic peptide. Furthermore, the circular data augmentation method was used, and the circular convolution method CyclicConv was devised to express the cyclic structure. CycPeptPPB exhibited excellent performance, with mean absolute error (MAE) of 4.79% and correlation coefficient (R) of 0.92 for the public drug dataset, compared to the prediction performance of the existing PPB rate prediction software (MAE=15.08%, R=0.63). AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the online supplementary material. The source code of CycPeptPPB is available at https://github.com/akiyamalab/cycpeptppb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI： 10.1093/bioinformatics/btab726

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記述言語：英語  

DOI： 10.3389/fmolb.2022.875859

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担当区分：最終著者,　責任著者   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/CIBCB55180.2022.9863032

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その他リンク： https://dblp.uni-trier.de/db/conf/cibcb/cibcb2022.html#FuruiO22

掲載種別：論文集(書籍)内論文   出版者・発行元：Springer International Publishing  

DOI： 10.1007/978-3-030-69984-0_53

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

DOI： 10.3390/ijms222010925

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/genes12091455

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society (ACS)  

Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exchange umbrella sampling simulations and predicted membrane permeability using the inhomogeneous solubility-diffusion model and a modified version of it. Furthermore, we confirmed the effectiveness of this protocol by predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical structures, including some confidential designs from a pharmaceutical company. As a result, a reasonable correlation between experimentally assessed and calculated membrane permeability of cyclic peptides was observed for the peptide libraries, except for strongly hydrophobic peptides. Our analysis of the MD trajectory demonstrated that most peptides were stabilized in the boundary region between bulk water and membrane and that for most peptides, the process of crossing the center of the membrane is the main obstacle to membrane permeation. The height of this barrier is well correlated with the electrostatic interaction between the peptide and the surrounding media. The structural and energetic features of the representative peptide at each vertical position within the membrane were also analyzed, revealing that peptides permeate the membrane by changing their orientation and conformation according to the surrounding environment.

DOI： 10.1021/acs.jcim.1c00380

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

DOI： 10.3390/ijms22105298

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担当区分：筆頭著者,　責任著者   掲載種別：研究論文（学術雑誌）  

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その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2105.html#abs-2105-03617

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Cyclic peptides, with unique structural features, have emerged as new candidates for drug discovery; their association with human serum albumin (HSA; long blood half-life) is crucial to improve drug delivery and avoid renal clearance. Here, we present the crystal structure of HSA complexed with dalbavancin, a clinically used cyclic peptide. Small-angle X-ray scattering and isothermal titration calorimetry experiments showed that the HSA-dalbavancin complex exists in a monomeric state; dalbavancin is only bound to the subdomain IA of HSA in solution. Structural analysis and MD simulation revealed that the swing of Phe70 and movement of the helix near dalbavancin were necessary for binding. The flip of Leu251 promoted the formation of the binding pocket with an induced-fit mechanism; moreover, the movement of the loop region including Glu60 increased the number of noncovalent interactions with HSA. These findings may support the development of new cyclic peptides for clinical use, particularly the elucidation of their binding mechanism to HSA.

DOI： 10.1021/acs.jmedchem.0c01578

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掲載種別：研究論文（学術雑誌）  

DOI： 10.14529/js200102

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掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Springer  

DOI： 10.1007/978-3-030-48842-0_2

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その他リンク： https://dblp.uni-trier.de/db/conf/scfa/scfa2020.html#AoyamaWOA20

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

Scoring is a challenging step in protein-protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

DOI： 10.3389/fmolb.2020.559005

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2142/biophysico.BSJ-2019050

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

<title>Abstract</title>Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using <italic>in silico</italic> methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

DOI： 10.1038/s41598-019-55069-y

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その他リンク： http://www.nature.com/articles/s41598-019-55069-y

掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3365610.3368409

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jmgm.2019.07.009

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担当区分：筆頭著者   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/BIBE.2019.00035

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その他リンク： https://dblp.uni-trier.de/db/conf/bibe/bibe2019.html#OhueYIA19

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.bbrep.2019.01.008

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society  

DOI： 10.1021/acs.orglett.8b03500

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.

DOI： 10.1007/s11030-018-9842-3

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担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）  

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その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-01103

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1186/s12859-018-2529-z

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Tokyo  

DOI： 10.1007/s10015-017-0416-8

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier Ltd  

DOI： 10.1016/j.compbiolchem.2018.03.013

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：BioMed Central Ltd.  

DOI： 10.1186/s12859-018-2073-x

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier Ltd  

DOI： 10.1016/j.compbiolchem.2018.02.008

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier Inc.  

DOI： 10.1016/j.jmgm.2017.11.011

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Cellular transitions and differentiation processes require mRNAs supporting the new phenotype but also the clearance of existing mRNAs for the parental phenotype. Cellular reprogramming from fibroblasts to induced pluripotent stem cells (iPSCs) occurs at the early stage of mesenchymal epithelial transition (MET) and involves drastic morphological changes. We examined the molecular mechanism for MET, focusing on RNA metabolism. DDX6, an RNA helicase, was indispensable for iPSC formation, in addition to RO60 and RNY1, a non-coding RNA, which form complexes involved in intracellular nucleotide sensing. RO60/RNY1/DDX6 complexes formed prior to processing body formation, which is central to RNA metabolism. The abrogation of DDX6 expression inhibited iPSC generation, which was mediated by RNA decay targeting parental mRNAs supporting mesenchymal phenotypes, along with microRNAs, such as miR-302b-3p. These results show that parental mRNA clearance is a prerequisite for cellular reprogramming and that DDX6 plays a central role in this process.

DOI： 10.1371/journal.pone.0203708

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1093/bioinformatics/btx178

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/10_2016_41

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICCABS.2017.8114299

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その他リンク： https://dblp.uni-trier.de/db/conf/iccabs/iccabs2017.html#BanOA17

記述言語：英語   掲載種別：論文集(書籍)内論文   出版者・発行元：Humana Press Inc.  

DOI： 10.1007/978-1-4939-7015-5_2

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Springer Verlag  

DOI： 10.1007/978-3-319-63312-1_48

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2142/biophysico.13.0_105

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1186/1752-0509-9-S1-S6

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1093/bioinformatics/btu532

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   出版者・発行元：一般社団法人 日本生物物理学会  

DOI： 10.2142/biophys.54.S240_1

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：BioMed Central Ltd.  

DOI： 10.1186/1753-6561-7-S7-S6

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1186/1751-0473-8-18

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記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0069365

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2506583.2506693

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2506583.2506669

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2506583.2506670

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-642-34123-6_16

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jmb.2011.09.031

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：1  

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記述言語：日本語   出版者・発行元：Information Processing Society of Japan (IPSJ)  

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

DOI： 10.1016/j.bpj.2018.11.3026

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記述言語：日本語  

タンパク質の複合体構造を予測するドッキングシミュレーションは創薬において重要な役割を果たす.タンパク質は複合体を形成する際に立体構造の一部が変化することが知られているが,ドッキングシミュレーションの際にそのような分子の柔軟性を考慮すると,候補構造の探索空間が膨大になる問題があった.そこで本研究ではゲーミフィケーションにより,探索の効率化を実現する方法を提案する.具体的には,生物物理学の知識を持たないユーザーであっても,直感的に分子表面の側鎖を動かしてより良いドッキング状態を形成可能な仕組みを備えたゲームソフトを実装し,多数のプレイヤーを競わせることで高い精度で複合体構造を予測する.本研究の利用により,計算機が自動で探索を行う旧来の手法と比較して,高精度の予測を短時間で達成できることが期待できる.

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

DOI： 10.1016/j.bpj.2016.11.2418

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

DOI： 10.1016/j.bpj.2015.11.1759

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

DOI： 10.1016/j.bpj.2014.11.2579

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

タンパク質ドッキング計算は通常2つのタンパク質の複合体構造を予測する手法であるが,3体以上のドッキング計算やタンパク質間相互作用ネットワークの解析などに応用可能性を持つ.一方でこれらの応用を実現するためには膨大な回数のドッキング計算を要するため,多大な計算リソースと,またそれを充分に活用することのできるシステムの開発が求められていた.本発表ではGPUクラスタ上で並列計算が実行可能なタンパク質ドッキング計算システムであるMEGADOCK 4.0を紹介する.本システムの並列性能を,ノードあたり12CPUコアと3GPUを備えたTSUBAME 2.5スーパーコンピュータで測定し,35ノード実行に対する420ノード実行時の強スケーリング値0.98を達成した.また,100万件のタンパク質ドッキング計算が,420ノードの利用によって約半日で完了することを確認した.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

タンパク質ドッキング計算は通常2つのタンパク質の複合体構造を予測する手法であるが,3体以上のドッキング計算やタンパク質間相互作用ネットワークの解析などに応用可能性を持つ.一方でこれらの応用を実現するためには膨大な回数のドッキング計算を要するため,多大な計算リソースと,またそれを充分に活用することのできるシステムの開発が求められていた.本発表ではGPUクラスタ上で並列計算が実行可能なタンパク質ドッキング計算システムであるMEGADOCK 4.0を紹介する.本システムの並列性能を,ノードあたり12CPUコアと3GPUを備えたTSUBAME 2.5スーパーコンピュータで測定し,35ノード実行に対する420ノード実行時の強スケーリング値0.98を達成した.また,100万件のタンパク質ドッキング計算が,420ノードの利用によって約半日で完了することを確認した.

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

タンパク質ドッキング計算は通常 2 つのタンパク質の複合体構造を予測する手法であるが，3 体以上のドッキング計算やタンパク質間相互作用ネットワークの解析などに応用可能性を持つ．一方でこれらの応用を実現するためには膨大な回数のドッキング計算を要するため，多大な計算リソースと，またそれを充分に活用することのできるシステムの開発が求められていた．本発表では GPU クラスタ上で並列計算が実行可能なタンパク質ドッキング計算システムである MEGADOCK 4.0 を紹介する．本システムの並列性能を，ノードあたり 12CPU コアと 3GPU を備えた TSUBAME 2.5 スーパーコンピュータで測定し，35 ノード実行に対する 420 ノード実行時の強スケーリング値 0.98 を達成した．また，100 万件のタンパク質ドッキング計算が，420 ノードの利用によって約半日で完了することを確認した．The application of protein-protein docking to the large-scale interactome analysis, the treatment of protein flexibility or multiple protein-protein docking problem are current challenges in structural bioinformatics that require huge computing resource. In this work we present MEGADOCK 4.0, an FFT-based docking software which makes extensive use of recent GPU supercomputers and show the powerful scalable performance of over 97% strong scaling with TSUBAME 2.5 supercomputing system. In addition, a million protein-protein docking jobs can be calculated about a half day by using 420 nodes of TSUBAME 2.5.

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

DOI： 10.1016/j.bpj.2013.11.2310

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我々はタンパク質間相互作用ネットワークを予測することを目的としてタンパク質間ドッキング計算に基づいたタンパク質間相互作用予測手法を開発している．従来相互作用予測を行う際は，予測対象のタンパク質ペアの情報のみを用いて相互作用評価値の計算を行っていたが，本研究ではよりネットワーク予測に適した手法として，対象ペアが，他の相互作用候補ペアと比べてどの程度相互作用し得るかを相対的に評価することで予測精度を向上させることに成功した．We have developed a method to predict protein-protein interaction based on docking calculation for getting information of protein-protein interaction networks. In this study, we propose a new method to predict a protein-protein interaction network. Our proposed method uses not only the docking results of a pair but also the results of the other protein pairs including a protein of the target pair. With the new method, we succeeded to improve the accuracy of the prediction of protein-protein interaction networks.

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記述言語：日本語  

我々はタンパク質間相互作用ネットワークを予測することを目的としてタンパク質間ドッキング計算に基づいたタンパク質間相互作用予測手法を開発している．従来相互作用予測を行う際は，予測対象のタンパク質ペアの情報のみを用いて相互作用評価値の計算を行っていたが，本研究ではよりネットワーク予測に適した手法として，対象ペアが，他の相互作用候補ペアと比べてどの程度相互作用し得るかを相対的に評価することで予測精度を向上させることに成功した．We have developed a method to predict protein-protein interaction based on docking calculation for getting information of protein-protein interaction networks. In this study, we propose a new method to predict a protein-protein interaction network. Our proposed method uses not only the docking results of a pair but also the results of the other protein pairs including a protein of the target pair. With the new method, we succeeded to improve the accuracy of the prediction of protein-protein interaction networks.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

タンパク質間ドッキング予測ソフトウェアMEGADOCKでは,従来は形状相補性と静電相互作用の2つの効果を評価関数としていたが,本研究では新たにAtomic Contact Energyによる疎水性相互作用モデルを提案し,MEGADOCKに追加した.MEGADOCKなどのFFTを用いて計算されるグリッドベースのドッキング予測では,通常3つの効果を計算するために2回以上の相関関数計算を要するが,提案手法ではレセプターのみを考慮する新しい簡易型スコア関数によって,3つの効果を1回のFFT計算で同時に計算することが可能となり,高速性を損なわずに精度を向上させることに成功した.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

タンパク質の構造情報を利用したタンパク質間相互作用(Protein-Protein Interaction, PPI)予測を行うために,我々はタンパク質ドッキング計算に基づくPPI予測手法の開発を行ってきた.本研究ではタンパク質ドッキング計算によって得られた複合体候補構造群について,それらがどのように3次元空間上で分布しているかを可視化する手法と,タンパク質の各残基の相互作用している傾向を可視化する手法の2種類を開発した。また,可視化結果から得た知見をもとに,相互作用予測結果から,本来は相互作用しないのに&quot;相互作用する&quot;と判定された結果を識別する評価値を導入し,それを利用したPPI予測精度の改善手法を開発した.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

タンパク質-RNA間相互作用(protein-RNA interaction, PRI)は,細胞システムの理解に重要であり,複数のタンパク質とRNAの相互作用関係を網羅的に予測する手法の確立が求められている.我々は立体構造情報を用いたタンパク質問相互作用予測システムMEGADOCKを開発してきたが,本研究ではMEGADOCKに対してCHARMM27核酸原子パラメータを導入することで,RNA立体構造を扱えるように拡張した.Protein Data Bankより選出した78のRNA結合タンパク質による78×78件の網羅的PRI予測を行い,元の構造に相当する78ペアを正例,その他の6,006ペアを負例として評価を行った結果,F値0.465での予測に成功した.

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記述言語：日本語  

タンパク質-RNA間相互作用は遺伝子発現調節などのシステム的理解に重要であり，その予測は生命情報科学の大きな課題である．我々はタンパク質の立体構造情報を用いたタンパク質間相互作用ネットワークの予測に取り組んできたが，本研究ではRNA結合タンパク質に着目し，従来タンパク質同士の計算に用いられていた高速ドッキング計算手法をRNAも扱えるように拡張した．また，本手法によって大量の複合体候補構造を生成し，それらの結合エネルギーに基づくリランキングを用いたタンパク質-RNA間相互作用予測手法を提案する．Protein Data Bankに含まれるRNA結合タンパク質78例を用いた78×78通りの網羅的予測に本手法を適用した結果，F値0.52での予測に成功した．

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記述言語：日本語   出版者・発行元：東京工業大学学術国際情報センター  

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記述言語：日本語   出版者・発行元：情報処理学会  

タンパク質間相互作用 （Protein-Protein Interaction，PPI） に関するネットワークの解明は，細胞システムの理解や構造ベース創薬に重要な課題であり，網羅的 PPI 予測手法の確立が求められている．タンパク質立体構造データ群から網羅的に相互作用の可能性を予測するために，我々は立体形状の相補性と物理化学的性質に基づくタンパク質ドッキングの手法を研究してきた．本研究のプロジェクトの一環として新たに開発した MEGADOCK システムは，高速なドッキング計算を行うための様々な工夫を取り入れており，なかでも rPSC スコアと呼ぶスコア関数は，既存ツールの ZDOCK と比べて同等の精度を維持しながらも約 4 倍の速度向上を実現し，網羅的計算を現実のものとした．本論文では MEGADOCK システムの構成および計算モデルについて述べる．ベンチマークデータセットに適用した結果，従来手法を大きく上回る最大 F 値 0.415 を得た．さらにシステム生物学の典型的な問題の 1 つである細菌走化性シグナル伝達系のタンパク質群に MEGADOCK を応用した．その結果，既知の相互作用の再現をベンチマークデータと同等の精度 （F 値 0.436） で行うことに成功し，かつ生物学的に相互作用の可能性が高い組合せであるにもかかわらず，現在までに報告されていないものとして，CheY タンパク質と CheD タンパク質の相互作用の可能性を示唆した．The elucidation of the protein-protein interaction (PPI) network is an important problem in the understanding of the cellular system and structure-based drug design. An effective way to conduct exhaustive PPI screening is one of the computational solutions for this problem. To predict all-to-all PPI from protein structures, we have been studying the protein docking approach based on the physico-chemical properties and shape complementarity. To realize these procedures that require huge number of protein dockings, we have developed high-speed protein-protein docking software "MEGADOCK" that reduces calculation time needed for docking by using several techniques that include a novel scoring function called rPSC. MEGADOCK was shown to be capable of exhaustive PPI screening by making docking calculation four times faster than conventional docking software, ZDOCK, while keeping almost the same level of accuracy in docking predictions. Here we describe an architecture and calculation model of MEGADOCK. We yielded F-measure value of 0.415 which substantially higher than previous studies when our PPI prediction system was applied to a general benchmark data. The same prediction system was applied to bacterial chemotaxis pathway, which is a typical basic problem in systems biology, and obtained the same level of accuracy (F-measure value of 0.436) with the benchmark dataset. We also found some interactions such as CheY--CheD among "False-Positives" that were worthy of further analysis.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00070731/

記述言語：日本語   出版者・発行元：情報処理学会  

タンパク質立体構造データ群から網羅的にタンパク質間相互作用 （PPI） の可能性を予測するために，我々は立体形状の相補性と物理化学的性質に基づくタンパク質ドッキングの手法を研究してきた．本研究のプロジェクトの一環として新たに開発した MEGADOCK システムは，高速なドッキング計算を行うための様々な工夫を取り入れており，中でも rPSC スコアと呼ぶスコア関数は，既存ツールの ZDOCK と比べて同等の精度を維持しながらも約 4 倍の速度向上を実現し，網羅的計算を現実のものとした．本論文では MEGADOCK システムの構成および計算モデルについて述べる．ベンチマークデータセットに適用した結果，従来手法を大きく上回る最大 F 値 0.415 を得た．さらにシステム生物学の典型的な問題の 1 つである細菌走化性シグナル伝達系のタンパク質群に MEGADOCK を応用した結果，既知の相互作用の再現をベンチマークデータと同等の精度 （F 値 0.436） で行うことに成功し，かつ生物学的に相互作用の可能性が高い組み合わせであるにも関わらず，現在までに報告されていない CheY タンパク質と CheD タンパク質の相互作用の可能性を示唆した．An effective way to conduct exhaustive Protein-Protein Interaction (PPI) screening is one of the computational solutions for this problem. To predict all-to- all PPI from protein structures, we have been studying the protein docking approach based on the physico-chemical properties and shape complementarity. To make these procedures that require huge number of protein dockings, we have developed high-speed protein-protein docking software "MEGADOCK" that reduces calculation time needed for docking by using several techniques that include a novel scoring function called rPSC. MEGADOCK was shown to be capable of exhaustive PPI screening by making docking calculation four times faster than conventional docking software, ZDOCK, while keeping almost the same level of accuracy in docking predictions. Here we describe an architecture and calculation model of MEGADOCK. We yielded F-measure value of 0.415 which substantially higher than previous studies when our PPI prediction system was applied to a general benchmark data. The same prediction system was applied to bacterial chemotaxis pathway, which is a typical basic problem in systems biology, and obtained the same level of accuracy (F-measure value of 0.436) with the benchmark dataset. We also found some interactions such as CheY-CheD among "False-Positives" that were worthy of further analysis.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00069426/

記述言語：日本語   出版者・発行元：情報処理学会  

タンパク質間相互作用 (Protein-Protein Interaction : PPI) ネットワークの解明は細胞システムの理解や構造ベース創薬に重要な課題であり，網羅的 PPI 予測手法の確立が求められている．タンパク質立体構造群から網羅的に相互作用を予測するために，我々は立体形状の相補性と物理化学的性質に基づくタンパク質ドッキングの手法を研究してきた．本研究ではドッキング予測構造の持つエネルギー値によるリランキングを行い，ドッキング予測精度を向上させ，その技法を 44×44 のタンパク質相互作用の総当たり解析に応用して性能を評価した．その結果，従来よりも高精度な網羅的相互作用予測が可能になったことを示す．The elucidation of the protein-protein interaction (PPI) network is an important problem in the understanding of the cellular system and structure-based drug design. To predict all-to-all PPI from protein structures, we have been studying the protein docking approach based on the physical/chemical properties and shape complementarity. In this study, we employ a re-ranking method at the docking structure ranking stage based on energy values of decoys and have succeeded to improve the accuracy of docking prediction. In addition, we applied the proposed method to predict PPIs in an all-to-all fashion. As a result of application to 44×44 all-to-all PPI analysis, we obtained an improved performance than previous methods.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00068042/

J-GLOBAL

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J-GLOBAL

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語   出版者・発行元：情報処理学会  

タンパク質間相互作用 (Protein-Protein Interaction : PPI) ネットワークの解明は細胞システムの理解や構造ベース創薬に重要な課題であり，網羅的 PPI 解析法の確立が求められている．我々は網羅的タンパク質ドッキングシステム "MEGADOCK" を開発してきたが，その予測精度は充分ではなく，改善が望まれていた．本稿では，実数のみで表わされる新たな形状相補性モデルである rPSC (real Pairwise Shape Complementarity) を提案し，さらに静電的相互作用を組み合わせることで，計算時間の大幅な増加を招くことなく精度の向上に成功したことを示す．The elucidation of the protein-protein interaction (PPI) network is an important problem in the understanding of the cellular system and structure-based drug design. The establishment of the all-to-all PPI analytical method is also a highly demanded task. We developed an all-to-all protein docking system "MEGADOCK" for this purpose. In this study, we propose a new shape complementarity model rPSC (real Pairwise Shape Complementarity) to improve prediction of MEGADOCK. We also added electrostatic interaction to the imaginary term of the scoring function. We successfully improved the precision without causing large increase of calculation time.

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その他リンク： http://id.nii.ac.jp/1001/00062131/

J-GLOBAL

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：ポスター発表  

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：ポスター発表  

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出願人：国立大学法人東京工業大学

出願番号：特願2021-023750  出願日：2021年2月

公開番号：特開2022-078924  公開日：2022年5月

J-GLOBAL

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出願人：アヘッド・バイオコンピューティング株式会社

出願番号：特願2021-023750  出願日：2021年2月

公開番号：特開2022-078924  公開日：2022年5月

特許番号/登録番号：特許第7125575号  登録日：2022年8月 

J-GLOBAL

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