Hiromasa Kaneko

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 42

Top Topics

Molecular Dynamics

Structure Activity Relationship

Machine Learning

Wastewater Treatment

Top Venues

Analytical science advances

Journal of chemical information and modeling

Molecular informatics

This page only lists publications with an associated author ORCID identifier.

Publications

Hiromasa Kaneko
Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models. ACS omega 9 (10) (2024)
Yuki Nakayama, Saki Morishita, Hayato Doi, Tatsuya Hirano, Hiromasa Kaneko
Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning. ACS omega 9 (16) (2024)
Yuki Nakayama, Saki Morishita, Hayato Doi, Tatsuya Hirano, Hiromasa Kaneko
Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning. ACS omega 9 (16) (2024)
Hiromasa Kaneko
Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models. ACS omega 9 (10) (2024)
Hiromasa Kaneko
Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance. ACS omega 8 (25) (2023)
Kohei Motojima, Abhijit Sen, Yoichi M A Yamada, Hiromasa Kaneko
Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions. Journal of chemical information and modeling (2023)
Hiromasa Kaneko
Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR Based on SELFIES. ACS omega 8 (24) (2023)
Hiromasa Kaneko
Defect rate prediction and failure-cause diagnosis in a mass-production process for precision electric components. Analytical science advances 4 (9-10) (2023)
Kohei Nemoto, Hiromasa Kaneko
De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. Journal of chemical information and modeling (2023)
Hiromasa Kaneko
Defect rate prediction and failure-cause diagnosis in a mass-production process for precision electric components. Analytical science advances 4 (9-10) (2023)
Kohei Nemoto, Hiromasa Kaneko
De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. Journal of chemical information and modeling 63 (3) (2023)
Yuki Nakayama, Hiromasa Kaneko
Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization. ACS omega 8 (32) (2023)
Toshiharu Morishita, Hiromasa Kaneko
Enhancing the Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory. ACS omega 8 (36) (2023)
Hiromasa Kaneko
Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR Based on SELFIES. ACS omega 8 (24) (2023)
Sota Aoi, Shingo Hirose, Wakana Soeda, Hiromasa Kaneko, Kunal S Mali, Steven De Feyter, Kazukuni Tahara
Spatially Controlled Aryl Radical Grafting of Graphite Surfaces Guided by Self-Assembled Molecular Networks of Linear Alkane Derivatives: The Importance of Conformational Dynamics. Langmuir : the ACS journal of surfaces and colloids (2023)
Kohei Nemoto, Hiromasa Kaneko
De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. Journal of chemical information and modeling 63 (3) (2023)
Kohei Nemoto, Hiromasa Kaneko
De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. Journal of chemical information and modeling 63 (3) (2023)
Shigeyoshi Samizo, Hiromasa Kaneko
Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors. ACS omega 8 (30) (2023)
Hiromasa Kaneko
Genetic Algorithm-Based Partial Least-Squares with Only the First Component for Model Interpretation. ACS omega 7 (10) (2022)
Ryo Iwama, Hiromasa Kaneko
Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO 2 Reduction. ACS omega 7 (50) (2022)
Toshiharu Morishita, Hiromasa Kaneko
Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds. ACS omega 8 (2) (2022)
Tatsuhito Ando, Naoto Shimizu, Norihisa Yamamoto, Nobuyuki N Matsuzawa, Hiroyuki Maeshima, Hiromasa Kaneko
Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization. The journal of physical chemistry. A 126 (36) (2022)
Yasuhiro Kanno, Hiromasa Kaneko
Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis. ACS omega 7 (2) (2022)
Ryo Iwama, Koji Takizawa, Kenichi Shinmei, Eisuke Baba, Noritoshi Yagihashi, Hiromasa Kaneko
Design and Analysis of Metal Oxides for CO 2 Reduction Using Machine Learning, Transfer Learning, and Bayesian Optimization. ACS omega 7 (12) (2022)
Toshiharu Morishita, Hiromasa Kaneko
Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides. ACS omega 7 (2) (2022)
Toshiharu Morishita, Hiromasa Kaneko
Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds. ACS omega 8 (2) (2022)
Ryo Iwama, Hiromasa Kaneko
Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO 2 Reduction. ACS omega 7 (50) (2022)
Tomoya Ebi, Abhijit Sen, Raghu N Dhital, Yoichi M A Yamada, Hiromasa Kaneko
Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts. ACS omega 6 (41) (2021)
Hiromasa Kaneko
Estimation and visualization of process states using latent variable models based on Gaussian process. Analytical science advances 2 (5-6) (2021)
Shunsuke Yuyama, Hiromasa Kaneko
Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks. Journal of chemical information and modeling 61 (12) (2021)
Hiromasa Kaneko
Estimation and visualization of process states using latent variable models based on Gaussian process. Analytical science advances 2 (5-6) (2021)
Hiromasa Kaneko, Shunsuke Kono, Akihiro Nojima, Takuya Kambayashi
Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP-Boruta. Analytical science advances 2 (9-10) (2021)
Kaina Shibata, Hiromasa Kaneko
Prediction of spin-spin coupling constants with machine learning in NMR. Analytical science advances 2 (9-10) (2021)
Kaina Shibata, Hiromasa Kaneko
Prediction of spin-spin coupling constants with machine learning in NMR. Analytical science advances 2 (9-10) (2021)
Hiromasa Kaneko, Shunsuke Kono, Akihiro Nojima, Takuya Kambayashi
Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP-Boruta. Analytical science advances 2 (9-10) (2021)
Kazukuni Tahara, Yuki Kubo, Shingo Hashimoto, Toru Ishikawa, Hiromasa Kaneko, Anton Brown, Brandon E Hirsch, Steven De Feyter, Yoshito Tobe
Porous Self-Assembled Molecular Networks as Templates for Chiral-Position-Controlled Chemical Functionalization of Graphitic Surfaces. Journal of the American Chemical Society 142 (16) (2020)
Fumika Nitta, Hiromasa Kaneko
Two- and Three-dimensional Quantitative Structure-activity Relationship Models Based on Conformer Structures. Molecular informatics 40 (3) (2020)
Hiroki Yoshihama, Hiromasa Kaneko
Design of thermoelectric materials with high electrical conductivity, high Seebeck coefficient, and low thermal conductivity. Analytical science advances 2 (5-6) (2020)
Hiroki Yoshihama, Hiromasa Kaneko
Design of thermoelectric materials with high electrical conductivity, high Seebeck coefficient, and low thermal conductivity. Analytical science advances 2 (5-6) (2020)
Hiromasa Kaneko
Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping. Molecular informatics 38 (3) (2018)
Hiromasa Kaneko
Sparse Generative Topographic Mapping for Both Data Visualization and Clustering. Journal of chemical information and modeling 58 (12) (2018)
Hiromasa Kaneko
Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels. Journal of chemical information and modeling 58 (2) (2018)