Kei Terayama 研究室

主宰者：Kei Terayama

理化学研究所・RIKEN Center for Advanced Intelligence Project

AI 要約（直近 5 年の研究成果）

Terayama研究室では、機械学習や人工知能の手法を用いて、物質・分子設計や材料開発における複雑な問題を解決する研究を行っています。研究の中心的な課題は、物質や分子の性質を予測したり、目的に合った新しい物質を自動的に設計したりすることです。具体的には、半導体素子の特性解析、医薬品候補物質の探索、環境に優しいプラスチックの開発など、多岐にわたる応用領域で活用されています。手法としては、大規模な計算シミュレーション（量子化学計算や分子動力学シミュレーション）の結果をもとに、深層学習や機械学習モデルを構築し、高速な予測や最適化を実現しています。スーパーコンピュータなど高性能計算システムを活用することで、従来は困難だった大規模データの処理分析を可能にしています。また、大規模言語モデルを活用して研究者の負担を軽減し、より使いやすいツール開発にも取り組んでいます。これらの研究を通じて、従来は実験や経験に頼ってきた物質開発を、データ駆動型の自動化された設計へと転換することを目指しています。生物資源管理や医療診断など、物質科学の枠を超えた応用事例も展開しており、機械学習が幅広い分野の課題解決に貢献できることを示しています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（69 件）

[2026] Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide‐Semiconductor Characterization
DOI: https://doi.org/10.1002/aisy.70437
[2025] Dietary tryptophan’s effect on lipid metabolism in the leopard coral grouper <i>Plectropomus leopardus</i>
DOI: https://doi.org/10.1016/j.csbr.2025.100077
[2025] Deep Learning-Assisted De Novo Molecule Identification through Vibrational Spectra
DOI: https://doi.org/10.26434/chemrxiv-2025-0vv7g
[2025] Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices
DOI: https://doi.org/10.1038/s43246-025-00946-5
[2025] Monocular burst swimming detection in Pacific bluefin tuna (Thunnus orientalis) using deep learning enhanced by ellipse fitting
DOI: https://doi.org/10.1007/s10499-025-02267-3
[2025] Data-driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability
DOI: https://doi.org/10.26434/chemrxiv-2025-24kkf
[2025] A machine learning approach to designing and understanding tough, degradable polyamides
DOI: https://doi.org/10.1038/s41524-025-01696-1
[2025] Comprehensive search for assessment indicators that influence the level of handwriting difficulties among children in educational settings
DOI: https://doi.org/10.1038/s41598-025-03634-z
[2025] Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning
DOI: https://doi.org/10.1186/s12911-025-03034-3
[2025] GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy
DOI: https://doi.org/10.1186/s13321-025-01003-6

続きを表示（残り 59 件）

[2025] Large language models open new way of AI-assisted molecule design for chemists
DOI: https://doi.org/10.1186/s13321-025-00984-8
[2025] A data-driven generative strategy to avoid reward hacking in multi-objective molecular design
DOI: https://doi.org/10.1038/s41467-025-57582-3
[2025] Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation
DOI: https://doi.org/10.1093/pnasnexus/pgaf094
[2025] BPS2025 - Deep learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation
DOI: https://doi.org/10.1016/j.bpj.2024.11.1790
[2025] Qcforever2: Advanced Automation of Quantum Chemistry Computations
DOI: https://doi.org/10.1002/jcc.70017
[2025] BPS2025 – Deep-learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation
DOI: https://doi.org/10.1016/j.bpj.2024.11.1865
[2024] AIPHAD, an active learning web application for visual understanding of phase diagrams
DOI: https://doi.org/10.1038/s43246-024-00580-7
[2024] Predicting condensate formation of protein and RNA under various environmental conditions
DOI: https://doi.org/10.1186/s12859-024-05764-z
[2024] Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science
DOI: https://doi.org/10.1038/s41598-024-76800-4
[2024] Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network
DOI: https://doi.org/10.1038/s41598-024-77893-7
[2024] Data-driven study of the enthalpy of mixing in the liquid phase
DOI: https://doi.org/10.1016/j.calphad.2024.102745
[2024] Rotifer detection and tracking framework using deep learning for automatic culture systems
DOI: https://doi.org/10.1016/j.atech.2024.100577
[2024] Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds
DOI: https://doi.org/10.1002/aisy.202400253
[2024] Combining three-dimensional acoustic coring and a convolutional neural network to quantify species contributions to benthic ecosystems
DOI: https://doi.org/10.1098/rsos.240042
[2023] Deep Learning-Based Differential Diagnosis of Follicular Thyroid Tumors Using Histopathological Images
DOI: https://doi.org/10.1016/j.modpat.2023.100296
[2023] <scp>ChemTSv2</scp> : Functional molecular design using de novo molecule generator
DOI: https://doi.org/10.1002/wcms.1680
[2023] AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data
DOI: https://doi.org/10.1186/s13321-023-00791-z
[2023] An efficient segmentation method based on semi-supervised learning for seafloor monitoring in Pujada Bay, Philippines
DOI: https://doi.org/10.1016/j.ecoinf.2023.102371
[2023] Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system
DOI: https://doi.org/10.1080/27660400.2023.2261834
[2023] Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules
DOI: https://doi.org/10.1021/acs.jctc.3c00764
[2023] Ranking Pareto optimal solutions based on projection free energy
DOI: https://doi.org/10.1103/physrevmaterials.7.093804
[2023] Individual health-disease phase diagrams for disease prevention based on machine learning
DOI: https://doi.org/10.1016/j.jbi.2023.104448
[2023] A framework to predict binary liquidus by combining machine learning and CALPHAD assessments
DOI: https://doi.org/10.1016/j.matdes.2023.112111
[2023] Semantic Segmentation of seafloor images in Philippines based on semi-supervised learning
DOI: https://doi.org/10.1109/ut49729.2023.10103432
[2023] Design of antimicrobial peptides containing non-proteinogenic amino acids using multi-objective Bayesian optimisation
DOI: https://doi.org/10.1039/d3dd00090g
[2023] Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations
DOI: https://doi.org/10.2142/biophysico.bppb-v20.0022
[2022] Extraction of Protein Dynamics Hidden in Cryo-EM Maps Using Deep Learning
DOI: https://doi.org/10.2142/biophys.62.193
[2022] Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning
DOI: https://doi.org/10.1016/j.ijpx.2022.100135
[2022] QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization
DOI: https://doi.org/10.1021/acs.jcim.2c00812
[2022] Monitoring of cage-cultured sea cucumbers using an underwater time-lapse camera and deep learning-based image analysis
DOI: https://doi.org/10.1016/j.atech.2022.100087
[2022] Automatic Rietveld refinement by robotic process automation with RIETAN-FP
DOI: https://doi.org/10.1080/27660400.2022.2146470
[2022] Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search
DOI: https://doi.org/10.1021/acs.jcim.2c00787
[2022] Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (Thunnus orientalis) eggs using deep learning
DOI: https://doi.org/10.1016/j.aquaeng.2022.102274
[2022] Correction to: Semi-automation of gesture annotation by machine learning and human collaboration
DOI: https://doi.org/10.1007/s10579-022-09599-z
[2022] Bayesian optimization package: PHYSBO
DOI: https://doi.org/10.1016/j.cpc.2022.108405
[2022] Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications
DOI: https://doi.org/10.1155/2022/6952999
[2022] Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring
DOI: https://doi.org/10.1080/14686996.2022.2075240
[2022] Machine-Learning-Based phase diagram construction for high-throughput batch experiments
DOI: https://doi.org/10.1080/27660400.2022.2076548
[2022] Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics
DOI: https://doi.org/10.1021/acs.jctc.1c01074
[2022] De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning
DOI: https://doi.org/10.1126/sciadv.abj3906
[2022] AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge
DOI: https://doi.org/10.1021/acs.jcim.1c01074
[2022] A machine learning–based classification approach for phase diagram prediction
DOI: https://doi.org/10.1016/j.matdes.2022.110497
[2022] Semi-automation of gesture annotation by machine learning and human collaboration
DOI: https://doi.org/10.1007/s10579-022-09586-4
[2022] Topological alternation from structurally adaptable to mechanically stable crosslinked polymer
DOI: https://doi.org/10.1080/14686996.2021.2025426
[2022] A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathological Diagnosis of Inflammatory Bowel Diseases
DOI: https://doi.org/10.1016/j.jcmgh.2022.07.001
[2021] Extraction of protein dynamics information from cryo-EM maps using deep learning
DOI: https://doi.org/10.1038/s42256-020-00290-y
[2021] CrySPY: a crystal structure prediction tool accelerated by machine learning
DOI: https://doi.org/10.1080/27660400.2021.1943171
[2021] Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule
DOI: https://doi.org/10.1016/j.scriptamat.2021.114335
[2021] Integrating Incompatible Assay Data Sets with Deep Preference Learning
DOI: https://doi.org/10.1021/acsmedchemlett.1c00439
[2021] Cost‐effective seafloor habitat mapping using a portable speedy sea scanner and deep‐learning‐based segmentation: A sea trial at Pujada Bay, Philippines
DOI: https://doi.org/10.1111/2041-210x.13744
[2021] PD42-10 DEVELOPMENT OF A DEEP-LEARNING–BASED SYSTEM TO DIAGNOSE UROTHELIAL CARCINOMA CELLS IN CYTOLOGY SPECIMENS
DOI: https://doi.org/10.1097/ju.0000000000002056.10
[2021] Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters
DOI: https://doi.org/10.1038/s41428-021-00531-w
[2021] Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization
DOI: https://doi.org/10.1021/acs.jctc.1c00301
[2021] Structure-Based <i>de Novo</i> Molecular Generator Combined with Artificial Intelligence and Docking Simulations
DOI: https://doi.org/10.1021/acs.jcim.1c00679
[2021] Pose Estimation of Swimming Fish Using NACA Airfoil Model for Collective Behavior Analysis
DOI: https://doi.org/10.20965/jrm.2021.p0547
[2021] Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis
DOI: https://doi.org/10.1063/5.0051902
[2021] A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens
DOI: https://doi.org/10.1002/cncy.22443
[2021] Black-Box Optimization for Automated Discovery
DOI: https://doi.org/10.1021/acs.accounts.0c00713
[2021] Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
DOI: https://doi.org/10.1038/s41598-020-80001-0

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（69 件）

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所属学会・役職（0 件）