Hiromasa Kaneko 研究室

主宰者：Hiromasa Kaneko

明治大学

AI 要約（直近 5 年の研究成果）

金子研究室は、化学・材料・製造プロセスの設計と最適化を支援する機械学習手法の開発に取り組んでいます。研究の中心的な課題は、新しい分子や材料の性質を予測し、実験やシミュレーションに基づくデータから効率よく設計条件を見つけ出すことです。医薬品の代謝特性から触媒活性、ポリマーの製造条件、センサー応答に至るまで、様々な物理化学的性質を予測する機械学習モデルを構築しています。手法としては、統計的な回帰モデルや深層学習の変分オートエンコーダなど複数のアルゴリズムを組み合わせ、分子構造の数値情報や分光スペクトルなどの実験データから特性を予測しています。特に、予測モデルの信頼性を評価する仕組みを重視しており、訓練データの範囲外での予測リスクを定量化する手法や、外れ値の性質を分類する方法を開発しています。また、逆解析により「目標とする性質を持つ材料・分子の設計条件は何か」という問いに直接答える枠組みも構築しています。これらの研究を通じて、実験効率の向上と資源の無駄削減を実現するデータ駆動型の設計手法を確立しています。医薬品開発、触媒設計、ガス分離膜や電池材料の開発、さらに製造プロセスの最適化など、産業応用を見据えた実践的な研究を展開しています。

※ 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（97 件）

[2026] Infrared Spectral Descriptors for Reaction Yield Prediction: Toward Redefining Experimental Spaces
DOI: https://doi.org/10.1002/minf.70019
[2026] Design of Gold Extraction Solvents Using Machine Learning Models
DOI: https://doi.org/10.1021/acsomega.5c11629
[2026] Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification
DOI: https://doi.org/10.1039/d5na01017a
[2026] Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models
DOI: https://doi.org/10.1021/acs.jcim.5c03220
[2026] Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions
DOI: https://doi.org/10.1007/s44211-026-00939-5
[2026] Beware of optimality criteria in design of experiments: New criterion based on distance to the ideal distribution for advanced machine learning
DOI: https://doi.org/10.1016/j.mtcomm.2026.114648
[2026] A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models
DOI: https://doi.org/10.1007/s44211-026-00924-y
[2026] Development of machine learning models to predict acetylcholinesterase inhibitory activity and UDP-glucuronosyltransferase affinity and design of new acetylcholinesterase inhibitors
DOI: https://doi.org/10.1016/j.rechem.2026.103321
[2026] Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework
DOI: https://doi.org/10.1007/s44211-026-00900-6
[2026] Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models
DOI: https://doi.org/10.1007/s44211-026-00904-2

続きを表示（残り 87 件）

[2026] Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO 2 Separation Membranes
DOI: https://doi.org/10.1002/minf.70032
[2026] Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization
DOI: https://doi.org/10.1021/acs.jcim.6c00193
[2026] A method to define a domain in chemical space where only stable molecular structures exist
DOI: https://doi.org/10.1016/j.rechem.2026.103270
[2026] Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties
DOI: https://doi.org/10.1007/s44211-026-00890-5
[2025] Enhancing the Interpretability of Asymmetric Catalysis CoMFA through PLS ( n = 1) with Contribution Map Merging and Spatial Aggregation
DOI: https://doi.org/10.1021/acs.joc.5c02293
[2025] Adsorption Energy Prediction Model for CO 2 Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements
DOI: https://doi.org/10.1021/acs.jcim.5c02182
[2025] Construction and Improvement of a Model for Quantifying Blood Glucose Concentration Using Mid‐Infrared Spectroscopy
DOI: https://doi.org/10.1002/cem.70091
[2025] Adaptive Design of Experimental Conditions for LaFeO3 Crystals with Experiments and Bayesian Optimization
DOI: https://doi.org/10.1021/acs.iecr.4c04680
[2025] Correlations between the constituent molecules, crystal structures, and dielectric constants in organic crystals
DOI: https://doi.org/10.1016/j.chemolab.2025.105376
[2025] Molecular Odor Prediction Using Olfactory Receptor Information
DOI: https://doi.org/10.1002/minf.202400274
[2025] Machine Learning Model for Predicting Dielectric Constant of Epoxy Resin with Additional Data Selection and Design of Monomer Structures for Low Dielectric Constant
DOI: https://doi.org/10.1021/acsapm.4c03279
[2025] Design of the Ethylbenzene production process using machine learning
DOI: https://doi.org/10.1016/j.cscee.2025.101157
[2025] Construction of Machine Learning Models to Predict the Maximum Absorption Wavelength Considering the Solute and Solvent and Inverse Analysis of the Models
DOI: https://doi.org/10.1021/acsomega.4c07490
[2025] Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model
DOI: https://doi.org/10.1002/minf.202400227
[2025] Data-driven exploration of layered double hydroxide crystals exhibiting high fluoride ion adsorption properties and chemical stability
DOI: https://doi.org/10.1039/d5ce00313j
[2025] kNNPC (k-Nearest Neighbor Algorithm per Class): Classification Method Predicting Extrapolation Regions with Reasonable Probability
DOI: https://doi.org/10.1002/cem.70135
[2025] Prediction of CFD Simulation Results Using Machine-Learning Models and Process Designs Based on Direct Inverse Analysis of the Models
DOI: https://doi.org/10.1021/acs.iecr.4c03669
[2025] Design of Polymeric Nickel Catalysts for Suzuki–Miyaura Type Cross–Coupling Reaction Using Machine Learning
DOI: https://doi.org/10.1021/acsapm.5c02957
[2025] Development of Pesticide-Likeness Scores and Models for Predicting Pesticide Activity of Molecular Scaffolds with Machine Learning
DOI: https://doi.org/10.1021/acsomega.5c07323
[2025] Development of a Model for Predicting the Thermophysical Properties of Carbon Materials and Proposal of Manufacturing Conditions Using the Model
DOI: https://doi.org/10.1002/ansa.70031
[2025] What Is the Outlier—Consistent Outlier or Inconsistent Outlier?
DOI: https://doi.org/10.1002/ansa.70030
[2025] Data analysis on yield and electrical properties of proton-conducting ceramic fuel cells
DOI: https://doi.org/10.1016/j.nexres.2025.100161
[2025] Distortion prediction model considering process types in film manufacturing process and identification of critical process variables
DOI: https://doi.org/10.1016/j.chemolab.2025.105474
[2025] Batch Process Design Including Initial and Operating Conditions and Online Property Estimation in Acrylic Resin Polymerization
DOI: https://doi.org/10.1021/acsomega.5c01274
[2025] Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results
DOI: https://doi.org/10.1002/ansa.70021
[2025] Development of a Model for Predicting the Adsorption Performance of Zeolites and Designing New Zeolites
DOI: https://doi.org/10.1021/acs.iecr.5c00225
[2025] A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials
DOI: https://doi.org/10.3390/molecules30081686
[2024] Prediction of bone formation rate of bioceramics using machine learning and image analysis
DOI: https://doi.org/10.1039/d3nj05991j
[2024] Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models
DOI: https://doi.org/10.1021/acsomega.3c08036
[2024] Clustering method for the construction of machine learning model with high predictive ability
DOI: https://doi.org/10.1016/j.chemolab.2024.105084
[2024] Predicting product quality and optimising process design using dynamic time warping in batch processes with varying batch times
DOI: https://doi.org/10.1016/j.cscee.2024.100655
[2024] Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses
DOI: https://doi.org/10.3390/ma17030571
[2024] Exploring Molecular Descriptors and Acquisition Functions in Bayesian Optimization for Designing Molecules with Low Hole Reorganization Energy
DOI: https://doi.org/10.1021/acsomega.4c09124
[2024] Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics
DOI: https://doi.org/10.1016/j.rineng.2024.103475
[2024] Adaptive soft sensor considering process state in film manufacturing process and identification of critical process variables
DOI: https://doi.org/10.1016/j.rechem.2024.101677
[2024] Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning
DOI: https://doi.org/10.1021/acsomega.4c00655
[2023] Machine Learning Model for Predicting the Material Properties and Bone Formation Rate and Direct Inverse Analysis of the Model for New Synthesis Conditions of Bioceramics
DOI: https://doi.org/10.1021/acs.iecr.3c00332
[2023] Development of New Molecular Descriptors Based on Flare Software Considering Three-Dimensional Chemical Structures
DOI: https://doi.org/10.1021/acs.iecr.3c02775
[2023] Simultaneous Design of Gas Separation Membranes and Schemes through Combined Process and Materials Informatics
DOI: https://doi.org/10.1021/acs.iecr.3c02444
[2023] Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba5Nb4O15 Crystals
DOI: https://doi.org/10.1021/acs.cgd.3c00828
[2023] Design of Ammonia Borane Dehydrogenation Catalysts Using Previous Study Data, Public Data, and Machine Learning
DOI: https://doi.org/10.1021/acs.iecr.3c02591
[2023] Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions
DOI: https://doi.org/10.1021/acs.jcim.3c01196
[2023] Enhancing the Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory
DOI: https://doi.org/10.1021/acsomega.3c04891
[2023] T-Gen: Time series data generator for inverse analysis of machine learning models
DOI: https://doi.org/10.1016/j.cscee.2023.100475
[2023] Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization
DOI: https://doi.org/10.1021/acsomega.3c02344
[2023] Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors
DOI: https://doi.org/10.1021/acsomega.3c02567
[2023] Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance
DOI: https://doi.org/10.1021/acsomega.3c03722
[2023] Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR Based on SELFIES
DOI: https://doi.org/10.1021/acsomega.3c01332
[2023] Defect rate prediction and failure‐cause diagnosis in a mass‐production process for precision electric components
DOI: https://doi.org/10.1002/ansa.202300019
[2023] Spatially Controlled Aryl Radical Grafting of Graphite Surfaces Guided by Self-Assembled Molecular Networks of Linear Alkane Derivatives: The Importance of Conformational Dynamics
DOI: https://doi.org/10.1021/acs.langmuir.2c03434
[2023] Retrosynthetic and Synthetic Reaction Prediction Model Based on Sequence‐to‐Sequence with Attention for Polymer Designs
DOI: https://doi.org/10.1002/mats.202300011
[2023] Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma
DOI: https://doi.org/10.1016/j.jbiosc.2023.01.005
[2023] Design of batch process with machine learning, feature extraction, and direct inverse analysis
DOI: https://doi.org/10.1016/j.cscee.2023.100308
[2023] De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models
DOI: https://doi.org/10.1021/acs.jcim.2c01298
[2023] Nanoscale chemical patterning of graphite at different length scales
DOI: https://doi.org/10.1039/d3nr00632h
[2022] Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds
DOI: https://doi.org/10.1021/acsomega.2c05145
[2022] Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies
DOI: https://doi.org/10.1021/acs.iecr.2c02729
[2022] Local interpretation of nonlinear regression model with k-nearest neighbors
DOI: https://doi.org/10.1016/j.dche.2022.100078
[2022] Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO2 Reduction
DOI: https://doi.org/10.1021/acsomega.2c06008
[2022] Direct prediction of the batch time and process variable profiles using batch process data based on different batch times
DOI: https://doi.org/10.1016/j.compchemeng.2022.108072
[2022] Cross‐validated permutation feature importance considering correlation between features
DOI: https://doi.org/10.1002/ansa.202200018
[2022] Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization
DOI: https://doi.org/10.1021/acs.jpca.2c05229
[2022] Design of adaptive soft sensor based on Bayesian optimization
DOI: https://doi.org/10.1016/j.cscee.2022.100237
[2022] Molecular design of monomers by considering the dielectric constant and stability of the polymer
DOI: https://doi.org/10.1002/pen.26058
[2022] Adaptive soft sensor based on transfer learning and ensemble learning for multiple process states
DOI: https://doi.org/10.1002/ansa.202200013
[2022] Design and Analysis of Metal Oxides for CO2 Reduction Using Machine Learning, Transfer Learning, and Bayesian Optimization
DOI: https://doi.org/10.1021/acsomega.2c00461
[2022] Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides
DOI: https://doi.org/10.1021/acsomega.1c06481
[2022] Genetic Algorithm-Based Partial Least-Squares with Only the First Component for Model Interpretation
DOI: https://doi.org/10.1021/acsomega.1c07379
[2022] Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis
DOI: https://doi.org/10.1021/acsomega.1c06607
[2022] True Gaussian mixture regression and genetic algorithm-based optimization with constraints for direct inverse analysis
DOI: https://doi.org/10.1080/27660400.2021.2024101
[2022] Symmetry and spacing controls in periodic covalent functionalization of graphite surfaces templated by self-assembled molecular networks
DOI: https://doi.org/10.1039/d2nr02858a
[2021] Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests
DOI: https://doi.org/10.2477/jccj.2020-0021
[2021] Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal–Organic Frameworks
DOI: https://doi.org/10.1021/acs.jcim.1c01205
[2021] Adaptive soft sensor ensemble for selecting both process variables and dynamics for multiple process states
DOI: https://doi.org/10.1016/j.chemolab.2021.104443
[2021] Lifting the limitations of Gaussian mixture regression through coupling with principal component analysis and deep autoencoding
DOI: https://doi.org/10.1016/j.chemolab.2021.104437
[2021] Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts
DOI: https://doi.org/10.1021/acsomega.1c04826
[2021] Control of electron acceleration process during merging start-up of spherical tokamak
DOI: https://doi.org/10.1088/1741-4326/ac2107
[2021] Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables
DOI: https://doi.org/10.1016/j.heliyon.2021.e07356
[2021] Estimating the reliability of predictions in locally weighted partial least‐squares modeling
DOI: https://doi.org/10.1002/cem.3364
[2021] Extended Gaussian mixture regression for forward and inverse analysis
DOI: https://doi.org/10.1016/j.chemolab.2021.104325
[2021] Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization
DOI: https://doi.org/10.1002/amp2.10085
[2021] Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP‐Boruta
DOI: https://doi.org/10.1002/ansa.202000177
[2021] Prediction of spin–spin coupling constants with machine learning in NMR
DOI: https://doi.org/10.1002/ansa.202000180
[2021] Estimation and visualization of process states using latent variable models based on Gaussian process
DOI: https://doi.org/10.1002/ansa.202000122
[2021] Industrial Data Analysis in Data Science
[2021] Machine Learning for Chemical and Chemical Engineering Data Analysis and Molecular Design and Materials Design
[2021] Research on Design of Molecules, Materials, and Processes Using Machine Learning
DOI: https://doi.org/10.5360/membrane.46.338

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AI 要約（直近 5 年の研究成果）

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研究成果（97 件）

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