| Poster No. |
Title |
First Author |
Affiliation |
Duty time* |
| P01 計算化学（分子計算） Computational Chemistry (Molecular Modeling) |
| P01-01☆ |
Hepatitis C Virus Drug Resistance Mechanism: Docking and Molecular Dynamics Study of NS5A-Drug Complex |
YAXUAN WANG |
Kagoshima University |
(A) |
| P01-02☆ |
Generation of Structural Ensemble of Linear Diubiquitin Based on PCS Experiments |
Yoshiki Yugami |
the department of science, Osaka Prefecture University |
(B) |
| P01-03 |
Investigation of the utility of steered MD in the prediction of binding affinity: a case study of HSP90 |
Chisato Kanai |
INTAGE Healthcare Inc. |
(A) |
| P01-04☆ |
Cross-reactivity of T cell receptors against HCoV through three-dimensional structure prediction |
Ao Kikuchi |
Yokohama City University |
(B) |
| P01-05☆ |
Prediction Method for Protein-Bound Conformation of Macrocycles |
Shoya Hamaue |
Daiichi Sankyo Co., Ltd. |
(A) |
| P01-06☆ |
Enhanced Prediction of Antigen-Antibody Complex Structures through Aggressive Structural Refinement by AlphaFold2 |
Seiya Tanaka |
Department of Applied Physics, Graduate School of Engineering, Nagoya University |
(B) |
| P01-07☆ |
Deep-Learning model for Predicting the Replacement of Water Molecule upon Ligand Binding |
Takashi Yoshidome |
Department of Applied Physics, Graduate School of Engineering, Tohoku University |
(A) |
| P01-08☆ |
Comprehensive docking simulations using AlphaFold2-based human olfactory receptors for odor prediction
|
Hirotada Kaneshiro |
Department of Systems Informatics, Graduate School of Systems Informatics, Kobe University |
(B) |
| P01-09 |
Generative Model for Protein Structural Ensembles Enhanced by Molecular Dynamics Simulation Data |
Shinji Iida |
Kitasato University |
(A) |
| P01-10☆ |
Epicatechin n-mers (n ≥ 5) adopt more compact conformations than catechin n-mers |
Toshiaki UEDA |
Graduate School of Science and Technology, Shinshu University |
(B) |
| P01-11☆ |
The Computational Study on the Secondary Structure Formation of Nascent Peptides Inside the Ribosome Tunnel with Biomolecular Environments Mimicking Model |
Takunori Yasuda |
Institute of Life and Environmental Sciences, University of Tsukuba |
(A) |
| P01-12☆ |
Kinetic Analysis of Membrane Permeation Process of Cyclic Peptides Using Markov State Models with Molecular Dynamics Simulations |
Kei Terakura |
Institute of Science Tokyo |
(B) |
| P01-13 |
Predicting Lysine Reactivity: Insights from Constant-pH MD Simulations and Experimental Correlation |
Osamu Ichihara |
Schrödinger KK |
(A) |
| P01-14☆ |
Protein Tertiary Structure Prediction with Fine-tuned AlphaFold2 for Ligand Virtual Screening |
Yuki Yasumitsu |
Institute of Science Tokyo |
(B) |
| P01-15☆ |
Dynamic Relationship Between the Entrance to the Ligand Binding Site and the Dimer Interface in MAO-B |
Yoshitaka Tadokoro |
KINDAI University |
(A) |
| P01-16☆ |
Conformational study of macrocyclic peptides in solvent by MD simulations to improve their membrane permeability |
Ekishin Yanagi |
The University of Tokyo |
(B) |
| P01-17☆ |
High-precision and Efficient Prediction of Intermolecular Interaction Energies Using Deep Learning on Quantum Chemical Calculation Data |
Yudai Kobayashi |
Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University |
(A) |
| P01-18☆ |
Mechanisms of Type-51 R-body conformational changes revealed by in silico methods |
Hiroaki Oheda |
Yokohama City Univ. |
(B) |
| P01-19☆ |
Molecular simulation analysis for nucleic acids |
Kenji Yamagishi |
Nihon University |
(A) |
| P01-20☆ |
Induced-Fit Posing (IFP): A new pose prediction tool for hit to lead stage of drug discovery |
Samuel Toba |
OpenEye, Cadence Molecular Sciences |
(B) |
| P01-21☆ |
Generation of a suitable structure for SBDD by AlphaFold2 via Genetic Algorithm Parameter Search |
Keisuke Uchikawa |
Institute of Science Tokyo |
(A) |
| »ページTOPへ |
| P02 計算化学（分子認識） Computational Chemistry (Molecular Recognition) |
| P02-01☆ |
Investigation of the Allosteric Binding Sites of ERK2 by Metadynamics Simulation |
Hajime Sugiyama |
Mitsubishi Chemical Corporation |
(A) |
| P02-02☆ |
PairMap: An Intermediate Insertion Approach to Improve Accuracy in Relative Free Energy Perturbation Calculations of Distant Compound Transformations |
Kairi Furui |
Institute of Science Tokyo |
(B) |
| P02-03☆ |
Fragment Molecular Orbital Calculations for Zinc-Containing smHDAC8 |
Siyun Wang |
Graduate school, Osaka university |
(A) |
| P02-04☆ |
Interaction Analysis between pHLA and TCR using MD Simulation and Fragment Molecular Orbital Calculation |
Suzu Itami |
Kindai University |
(B) |
| P02-05☆ |
NNP-based Force Field Optimization to Improve RBFEP Performance |
Junya Yamagishi |
Preferred Networks |
(A) |
| P02-06☆ |
SpatialPPI 2.0: Enhancing Protein-Protein Interaction Prediction Through Distance Matrix Analysis Using Link Regression in Graph Attention Networks |
WENXING HU |
Tokyo Institute of Technology |
(B) |
| P02-07☆ |
Development of RIKEN Natural Products Depository Database |
Xingmei Ouyang |
RIKEN |
(A) |
| P02-08☆ |
Machine learning based prediction of quantum mechanical interaction energy between amino acid residues using fragment molecular orbital method |
Tomohiro Sato |
RIKEN |
(B) |
| P02-09☆ |
Computational assessment of the binding mode of Verteporfin, an inhibitor targeting the YAP-TEAD protein-protein interaction |
Yurika Ikegami |
University of Tsukuba Graduate School |
(A) |
| P02-10☆ |
Case studies of deep learning-based molecular docking program in medicinal chemistry |
Kazuya Osumi |
Toray Industries, Inc. |
(B) |
| P02-11☆ |
Binding Affinity Prediction Through Unsupervised Learning of Protein-Ligand MD Trajectories |
Kodai Igarashi |
Institute of Science Tokyo |
(A) |
| P02-12☆ |
Preprocessing of FMO calculations and practical visualization of interaction energies for drug design |
Hirofumi Watanabe |
WithMetis Co., Ltd. |
(B) |
| P02-13☆ |
Prediction of quantum mechanical interactions between the ligand and each amino acid residue in protein-ligand complexes |
Ryosuke Kita |
Kyushu university |
(A) |
| P03 データサイエンス Data Science |
| P03-01☆ |
Disease Prediction from Small Sample Gut Microbiome Data |
Daiki Sakai |
Yamada Lab, Tokyo Institute of Technology School of Life Science and Technology |
(A) |
| P03-02☆ |
Biological Age Prediction Using a Deep Neural Network Based on Steroid Metabolic Pathways |
Zi Wang |
IPR, Osaka Univ. |
(B) |
| P03-03☆ |
A deep learning model for predicting chemical-induced rat hepatocellular necrosis using transcriptome data. |
Kouki Maebara |
Nagoya City University |
(A) |
| P03-04☆ |
Reaction-Aware Molecular Optimization Using Conditional Transformer and Reinforcement Learning |
Shogo Nakamura |
Tokyo Institute of Technology |
(B) |
| P03-05☆ |
Computational determination of SMARTS molecular query containment relationships |
Seiji Matsuoka |
RIKEN |
(A) |
| P03-06☆ |
Development of New data analysis platform for medicinal chemist in Daiichi Sankyo |
Takayuki Serizawa |
Daiichi Sankyo Co., Ltd. |
(B) |
| P03-07☆ |
Data augmentation method of chimeric protein sequences for fine-tuning of protein language models |
Kei Yoshida |
Hitachi, Ltd. |
(A) |
| P03-08☆ |
Predicting Chemical Roles Using Natural Language Processing on Database Descriptions |
Yuya Koide |
Yokohama National University |
(B) |
| P03-09☆ |
Large-scale single nucleus RNA-seq analysis of Lewy body diseases subtypes |
Supakorn Pongpakdee |
Osaka University |
(A) |
| P03-10☆ |
SAR analysis and visualization utilizing a fragment-based approach: Application to a public data analysis of Targeted Protein Degrader |
Hiroyuki Hakamata |
Daiichi Sankyo Co., Ltd. |
(B) |
| P03-11☆ |
CycPeptMP: Development of Membrane Permeability Prediction Model of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation |
Jianan Li |
Institute of Science Tokyo |
(A) |
| P03-12☆ |
Deep learning-based enzyme screening to identify orphan enzyme genes |
Keisuke Hirota |
Institute of Science Tokyo |
(B) |
| P03-13 |
Data-driven design of visible-light photoswitches using structural features |
Said Byadi |
Hokkaido University |
(A) |
| P03-14 |
Recent Developments of FMODB in 2024: Efforts Towards Utilization of FMO data |
Kikuko Kamisaka |
RIKEN |
(B) |
| P03-15☆ |
Development of the data management system to acquire the strategic data for AI |
Miwa Sato |
Hitachi, Ltd |
(A) |
| P03-16 |
Enhancing Biological Insights with TargetMine: Integration of Genomic Region Annotations |
Yi-An Chen |
National Institutes of Biomedical Innovation, Health and Nutrition |
(B) |
| P03-17☆ |
Natural product-like compound generation with chemical language models |
Koh Sakano |
Institute of Science Tokyo |
(A) |
| P03-18☆ |
Development of an Integrated Machine Learning Model Incorporating Compound-Protein Information for Design and Prediction of Small-Molecule Modulators of PPIs |
Tsubasa Nagae |
Yokohama City University |
(B) |
| P03-19☆ |
REALM: Region-Empowered Antibody Language Model for Antibody Property Prediction |
Toru Nishino |
FUJIFILM Corporation |
(A) |
| P03-20☆ |
Generalized Molecular Representation for Drug Discovery via Molecular Graph Latent Diffusion Autoencoder |
Daiki Koge |
Niigata University |
(B) |
| P03-21☆ |
Data utilization and DX talent development on in-house KNIME platform |
Toshiyuki Ohfusa |
Astellas Pharma Inc. |
(A) |
| P03-22☆ |
Astellas's Digital Transformation for Small Molecule Drug Discovery Research |
Takanobu Araki |
Astellas Pharma Inc. |
(B) |
| P03-23☆ |
Unraveling Microbiome Complexity: A Knowledge Graph Approach to Functional Interpretation in Drug Discovery |
Hirokazu Nishimura |
Mitsubishi Tanabe Pharma Corporation |
(A) |
| P03-24☆ |
Age prediction from DNA methylation data using machine learning |
Nagisa Matsuo |
Kanazawa University |
(B) |
| P03-25☆ |
Exchange System for Glycan Textual Notations Development to Integrate Various Glycan Databases and Improve Search Accuracy |
Hiromitsu Shimoyama |
The Noguchi Institute |
(A) |
| P03-26 |
Drug discovery study integrating compound generative AI and molecular docking |
Noriaki Okimoto |
RIKEN |
(B) |
| P03-27☆ |
Spike separation of high-gamma power in ECoG using peak detection |
Masato Sakagami |
Kanazawa University |
(A) |
| P03-28☆ |
Estimation of transmission routes of the COVID-19 BA.1.1.2 variant using McAN and 3D graph visualization |
Masafumi Saito |
Kanazawa University |
(B) |
| P03-29☆ |
A framework for enhanced de novo protein design using deep learning and bayesian optimization |
Shuto Hayashi |
Institute of Science Tokyo |
(A) |
| P03-30☆ |
Directional Graph Modelling for Solution Design and Experiment Automation |
Yusuke SAKAI |
RIKEN |
(B) |
| »ページTOPへ |
| P04 量子構造生命科学 Quantum-Structural Life Sscience |
| P04-01☆ |
Analysis of Kinase Binding Specificity of Staurosporine using the Fragment Molecular Orbital Method |
Ruri Mihata |
Osaka University |
(A) |
| P04-02☆ |
Dynamical Interaction Energy Analysis of Elastase in Each Reaction State: Insights from Molecular Dynamics and Fragment Molecular Orbital Calculations |
Shuhei Miyakawa |
Osaka University |
(B) |
| P04-03 |
Development of the Cryptic Site searching method with Mixed-solvent molecular dynamics and Topological data analyses methods |
Jun Koseki |
National Institute of Advanced Industrial Science and Technology |
(A) |
| P04-04☆ |
Analysis of HS-AFM images of proteins combining MD simulation and machine learning |
Katsuki Sato |
Department of Chemistry, Tokyo University of Science |
(B) |
| P05 ADME・毒性 ADMET/toxicity |
| P05-01☆ |
Multi-Task Deep Learning using Graph Convolutional Networks for Predicting the Unbound Fraction in Human, Mouse, and Rat Plasma |
Harutoshi Kato |
Mitsubishi Tanabe Pharma Corporation |
(A) |
| P05-02☆ |
Enhancing the Reliability of Machine Learning Predictions through Quantitative Evaluation of the Applicability Domain: A Case Study of Multi-Task Prediction Model of Unbound Fraction in Human, Mouse, and Rat Plasma |
Yuki Doi |
Mitsubishi Tanabe Pharma Corporation |
(B) |
| P05-03 |
Development of tools to enhance the extracting process of ADME activity information from the Common Technical Document (CTD) |
Masataka Kuroda |
National Institutes of Biomedical Innovation, Health and Nutrition |
(A) |
| P05-04☆ |
Improving the performance of prediction models for small datasets of cytochrome P450 inhibition with deep learning |
ELPRI EKA PERMADI |
Institute for Protein Research, Osaka University, Japan |
(B) |
| P05-05☆ |
Addressing Common Metabolism Problems in Drug Discovery with in Silico Methods |
Sumie Tajima |
HULINKS Inc. |
(A) |
| P05-06☆ |
In silico prediction of total clearance, volume of distribution, and half-life with deep learning |
Ryoko Terada |
Institute for protein research of Osaka University |
(B) |
| P05-07☆ |
Unbound Fraction Optimized Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach |
Yuki Umemori |
Axcekead Tokyo West Partners |
(A) |
| P06 バイオインフォマティックス Bioinformatics |
| P06-01☆ |
Cell State Analysis of Immune Cells in the Tumor Microenvironment with Deep Learning |
Jiaxin Li |
The University of Tokyo |
(A) |
| P06-02☆ |
A Novel Endometrial Cancer Patient Stratification Considering ARID1A Protein Expression and Function with Effective Use of Multi-omics Data |
JUNSOO SONG |
Institute for Protein Research, Osaka University |
(B) |
| P06-03☆ |
Single-Cell Transcriptome Analysis Reveals Roles of GABA Receptors in the Connectivity of Dorsal-Ventral Motor Neurons in C. elegans |
Xingran Wang |
Institute for Protein Research, Osaka University |
(A) |
| P06-04☆ |
Impact of Intramolecular Hydrogen Bonds on Permeability Glycoprotein Mediated Transportation |
Yulong Gou |
Osaka University, Insitute for Protein Research |
(B) |
| P06-05☆ |
Improved Method of Predicting Protein Allosteric Site Based on Atomistic Bond-to-bond Interaction by Using GNN |
Chaowen Ou |
Tokyo Institute of Technology |
(A) |
| P06-06☆ |
Development of RNA velocity method using numerical integration of ordinary differential equations |
Yuki Kobayashi |
Kyoto University |
(B) |
| P06-07☆ |
Compound Retrosynthesis Analysis Using Consensus Estimate |
Akira Shinohara |
Tokyo Institute of Technology |
(A) |
| P06-08☆ |
Development of docking simulation with high-speed graph neural network scoring function |
Kohei Hoashi |
Tokyo Institute of Technology |
(B) |
| P06-09 |
Investigation of the trends and the potential in drug development for rare and intractable diseases based on the KEGG NETWORK |
Mao Tanabe |
National Institutes of Biomedical Innovation, Health and Nutrition |
(A) |
| P06-10☆ |
Prediction of medium components for bacteria using deep Learning |
Ryuhi Sato |
Institute of Science Tokyo |
(B) |
| P06-11☆ |
Elucidation of Stabilization Mechanisms of Intrabodies Based on Statistical Thermodynamics |
Koki Hattori |
Chiba University |
(A) |
| »ページTOPへ |
| P07 創薬応用 Drug Discovery Application |
| P07-01☆ |
Development of Pre-Fragment-Based MMP Analysis |
Toshiaki Watanabe |
DAIICHI SANKYO CO., LTD. |
(A) |
| P07-02☆ |
Discovery of a new histone deacetylase 8 inhibitor using machine learning-aided drug screening |
Yasunobu Yamashita |
Osaka University |
(B) |
| P07-03 |
Open Source Program Github and Its Application in Drug Discovery |
Kiyoshi Hasegawa |
TECHNOPRO R&D company |
(A) |
| P07-04☆ |
Development of accurate in silico screening protocol based on protein structural fluctuation and drug binding mode |
Hiroto Terada |
Graduate School of Science, Osaka Metropolitan University |
(B) |
| P07-05☆ |
Development of Prediction Models for Membrane Permeability of Cyclic Peptides using 3D Descriptors obtained from Molecular Dynamics Simulations and 2D Descriptors |
Masatake Sugita |
Institute of Science Tokyo |
(A) |
| P07-06☆ |
De novo PROTAC linker design to enhance cell membrane permeability based on a data-driven method |
Yuki Murakami |
Yokohama City University |
(B) |
| P07-07 |
Scaling up Binding Free Energy Calculations: Integrating Free Energy Perturbation (FEP) and Active Learning to Prioritize Compound Designs |
Yunoshin Tamura |
Preferred Networks |
(A) |
| P07-08☆ |
A Dirichlet diffusion model for generation of high-quality antimicrobial peptide sequences |
Koichi Oki |
Nagoya University |
(B) |
| P07-09☆ |
Development of a Massive Fluorogenic Probe Library Based on Bayesian Optimization toward the Discovery of Novel Biomarker Enzymes |
Daiki Ishimoto |
Laboratory of Chemistry and Biology, Graduate School of Pharmaceutical Sciences, The University of Tokyo |
(A) |
| P07-10☆ |
Virtual validation and the efficient learning methods exploration in federated learning (FL) for drug development research |
Ziwei Zhou |
Institute for Protein Research, Osaka University |
(B) |
| P07-11☆ |
Structure and Interaction Analysis of Nucleic Acid Encapsulated ssPalm Lipid Nanoparticles by Multiscale Simulation. |
Naoko Konami |
Graduate School and School of Pharmaceutical Sciences, Osaka University |
(A) |
| P07-12☆ |
Natural-Product Screening Toward Discovery of Anti-Aging Glutaminase-1 Inhibitors. An Electronic-Structure Informatics Study |
Mio Yokoyama |
Kumamoto University |
(B) |
| P07-13☆ |
DiffInt: Integrating Explicit Hydrogen Bond Modeling into Diffusion Models for Structure-Based Drug Design |
Masami Sako |
Tokyo Institute of Technology |
(A) |
| P07-14☆ |
QUBO Problem Formulation of Fragment-Based Protein–Compound Flexible Docking |
Keisuke Yanagisawa |
Tokyo Institute of Technology |
(B) |
| P07-15☆ |
Acquisition of Bias Information for Protein-Ligand Docking by Mixed-Solvent Molecular Dynamics |
Kaho Akaki |
Institute of Science Tokyo |
(A) |
| P07-16☆ |
Development of a compound pre-screening method based on docking of fragments |
Shimizu Masayoshi |
Institute of Science Tokyo |
(B) |
| P07-17☆ |
Report on Participation in the Tox24 Challenge: Construction of a High-Accuracy QSAR Predictive Model for Transthyretin Activity |
Yuma Iwashita |
Laboratory of Medical Molecular Analysis, Meiji Pharmaceutical University |
(A) |
| P07-18☆ |
Lead generation of a V-ATPase inhibitor using molecular generative AI |
Taiyo Toita |
Yokohama City University |
(B) |
| P07-19☆ |
Exploring the Power of Structural Biology on Degrader Discovery |
Yifan Hu |
Biortus Biosciences Co. Ltd |
(A) |
| P07-20 |
Constructing a machine learning model for discriminating Urotensin-II receptor inhibitors and its application |
Kentaro Kawai |
Setsunan University |
(B) |
| P07-21☆ |
Reaction-conditioned variational autoencoder model for catalyst generation and catalytic performance prediction |
Apakorn Kengkanna |
Institute of Science Tokyo |
(A) |
| P07-22☆ |
Drug discovery research utilizing BROOD: A Fragment Replacement and Molecular Design tool |
KOSUKE MINAGAWA |
Daiichi Sankyo Co., Ltd. |
(B) |
| P07-23☆ |
A small molecule inhibitor that binds to the unstable state of its target kinase DYRK1A demonstrates slowly dissociation from the complex |
Sora Suzuki |
International Graduate Program for Agricultural and Biological Science Selection |
(A) |
| P07-24 |
Correlation Analysis of Excipient Modulated Viscosity of Monoclonal Antibody and Molecular Surface Patch Properties |
Yoshirou Kimura |
MOLSIS Inc. |
(B) |
| P07-25☆ |
Predicting Antibody Stability pH Values from Amino Acid Sequences: Leveraging Protein Language Models for Formulation Optimization |
Takuya Tsutaoka |
FUJIFILM Corporation |
(A) |
| P07-26 |
Development of a Platform for Crystal Structure Prediction of Drug Molecules |
Okimasa Okada |
Mitsubishi Tanabe Pharma Corporation |
(B) |
| P07-27☆ |
Automated molecular modeling and property assessment for ADCs |
Takashi Ikegami |
MOLSIS Inc. |
(A) |
| P07-28☆ |
Validation of the reproducibility of hit-to-candidate using ChemTS |
Tomoki Yonezawa |
Keio University |
(B) |
| P07-29☆ |
Automated Hit-to-Lead Optimization Using the SINCHO Protocol and ChemTS |
Genki Kudo |
University of Tsukuba |
(A) |
| P07-30☆ |
Application of Amino-Acid Mapping: Activity Prediction for Drug Discovery |
Yuka Matsumoto |
Fujifilm Corporation |
(B) |
| P07-31☆ |
Efficient Single Step Synthesizable Molecular Design using Wasserstein Autoencoder |
Jinzhe Zhang |
Preferred Networks Inc |
(A) |
| P07-32☆ |
Quantitative Assessment of Protein−Ligand Activity Prediction from 3D Docking Poses for Urate Transporter 1 |
MARTIN |
Hokkaido University |
(B) |
| P07-33☆ |
Development of an efficient compound 3D conformer search system based on relative position of fragments |
Tomoya Saito |
Institute of Science Tokyo |
(A) |
| P07-34☆ |
Molecular Properties Prediction by Contrastive Learning Using Graph Neural Network |
Koshiro Aoki |
Institute of Science Tokyo |
(B) |
| »ページTOPへ |
| P08 臨床インフォマティクス Clinical Application |
| P08-01☆ |
Predicting clinical laboratory test result related to urine tests in patients with type 2 diabetes mellitus with renal complications using clinical trial data |
Hiroki Adachi |
Chugai Pharmaceutical Co., Ltd. |
(A) |
| P08-02☆ |
Machine learning models for predicting cross-reactivity of beta-lactam antibiotic allergy |
Shoki Hoshikawa |
Faculty of Pharmaceutical Sciences, Setsunan University |
(B) |
| P09 分子ロボティクス Molecular Robotics |
| P09-01 |
Modular photostable fluorescent DNA blocks for tracking collective movements of motor proteins |
Ryota Sugie |
Mie University |
(A) |
| P09-02☆ |
Size-Selective Capturing of Exosomes Using DNA Tripods |
Ryosuke Iinuma |
JSR Life Sciences Corporation |
(B) |
| P09-03☆ |
Anisotropic Swarming of DNA Modified Microtubules Under UV Light |
Chung Wing Chan |
Graduate School of Science, Kyoto University |
(A) |
| P09-04☆ |
De novo protein design of suitable binders for DNA origami-based devices |
Hisashi Tadakuma |
ShanghaiTech University |
(B) |
| P09-05☆ |
Over the Membrane: Study of Nucleic Acid Sequence Transfer Using Cholesterol-Modified DNA |
Rinka Aoki |
Graduate School of Engineering, Tohoku University |
(A) |
| P09-06 |
Multi-reconfigurable DNA nanolattice guided by a combination of external stimuli |
Yuri Kobayashi |
Mie university |
(B) |
| P09-07 |
Construction of dual-responsive circular DNA origami nanoactuator |
Ryoya Sakaguchi |
Mie University |
(A) |
| P09-08☆ |
Evaluation of anticancer activity and investigation of cellular uptake mechanism of drug-loaded DNA Origami dendrimers for application to drug delivery system |
Koichi Tanimoto |
Kansai University |
(B) |
| P10 健康科学 Health Sciences |
| P10-01☆ |
Decision-making model to enhance subjective well-being through individualized lifestyle modifications based on counterfactual explanation |
Yunosuke Matsuda |
Bathclin Corporation |
(A) |
| P11 その他 Others |
| P11-01☆ |
Evaluation of Data-Driven Drug Discovery Approaches: Utilizing Redmine Ticket Management System for Tracking and Analyzing Activities |
Kosuke Takeuchi |
DAIICHI SANKYO CO., LTD. |
(A) |
| P11-02☆ |
Japanese Food Ontology Development |
Chihiro Higuchi |
National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN) |
(B) |
| P11-03 |
** Canceled ** |
|
| P11-04☆ |
Development of a model to predict the severity of systemic lupus erythematosus using LIFE Study data |
Kiyohiro Toyofuku |
Kyushu University |
(B) |
| P11-05☆ |
Exploring unexpected factors related to glaucoma onset in diabetes patients using LIFE Study data |
Kaito Sasaki |
Kyushu University |
(A) |
| P11-06☆ |
Survival Analysis of Chronic Kidney Disease Using Multi-Regional Data from the LIFE Study |
Hiromu Matsumoto |
Kyushu Unviersity |
(B) |
| P11-07 |
Data-driven search for diseases whose patient numbers are associated with weather variability using LIFE study data |
Kensei Orita |
Kyushu University |
(A) |
| P11-08☆ |
Analysis of interactions between fatty acid membranes with pH-dependent phase structures and nucleic acid monomers using Molecular Dynamics simulation |
Ryoji Abe |
Tokyo Institute of Technology |
(B) |
| P11-09☆ |
A Virtual Reality Platform for Molecular Dynamics Based on Unity Engine |
Yuhui Zhang |
Tokyo Institute of Technology |
(A) |
| P11-10 |
From Computer-Assisted Routine/Repeated ‘Automation’ to AI-Assisted Future-Oriented ‘Autonomous (Intelligent/Creative)’: Division and Impact of ‘Automation’ and ‘Autonomous’ in Research Contents |
Kohtaro Yuta |
In Silico Data,Ltd. |
(B) |