Takashi Yoshidome 研究室

AI 要約（直近 5 年の研究成果）

※ 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。

研究成果（17 件）

• [2026] Correction to “Deep GIST: Deep Learning Models for Predicting the Distribution of Hydration Thermodynamics around Proteins”
  DOI: https://doi.org/10.1021/acs.jcim.6c01684
• [2026] Structure-based prediction of gas diffusion property of catalytic layer of proton exchange membrane fuel cells via manifold learning and X-ray ptychographic nano-computed tomography
  DOI: https://doi.org/10.1016/j.jpowsour.2026.239916
• [2026] Deep GIST: Deep Learning Models for Predicting the Distribution of Hydration Thermodynamics around Proteins
  DOI: https://doi.org/10.1021/acs.jcim.5c02389
• [2025] Prediction of the hydration structures at the protein–protein Interface of dimers using deep learning
  DOI: https://doi.org/10.1016/j.cplett.2025.142372
• [2024] Interface-packing analysis of F1-ATPase using integral equation theory and manifold learning
  DOI: https://doi.org/10.1016/j.physa.2024.130201
• [2024] Extraction of molecular information from experimental data on liquids using manifold learning
  DOI: https://doi.org/10.1016/j.molliq.2024.126251
• [2023] Elucidating Conformational Dynamics and Thermostability of Designed Aromatic Clusters by Using Protein Cages
  DOI: https://doi.org/10.1002/chem.202300488
• [2023] A deep-learning model for the prediction of protein domains
  DOI: https://doi.org/10.1016/j.bpj.2022.11.928
• [2023] Four‐dimensional imaging for cryo‐electron microscopy experiments using molecular simulations and manifold learning
  DOI: https://doi.org/10.1002/jcc.27290
• [2023] Mechanism of a high free-energy transduction efficiency of Bacillus PS3 F1-ATPase from the perspective of solvent entropy
  DOI: https://doi.org/10.1016/j.molliq.2023.123346

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