K. Funatsu

发表

MassBank: a public repository for sharing mass spectral data for life sciences.

M. Hirai, S. Kanaya, Hideyuki Suzuki, 2010, Journal of mass spectrometry : JMS.

Feature selection by genetic algorithms for mass spectral classifiers

K. Varmuza, R. Leardi, K. Funatsu, 2001 .

Rethinking drug design in the artificial intelligence era

G. Schneider, J. Listgarten, A. Plowright, 2019, Nature Reviews Drug Discovery.

Exploring differential evolution for inverse QSAR analysis

J. Bajorath, K. Funatsu, Tomoyuki Miyao, 2017, F1000Research.

Exploring differential evolution for inverse QSAR analysis

J. Bajorath, K. Funatsu, Tomoyuki Miyao, 2017, F1000Research.

Ensemble locally weighted partial least squares as a just‐in‐time modeling method

K. Funatsu, H. Kaneko, 2016 .

Computer-assisted organic synthesis design and reaction prediction system, “AIPHOS”

S. Sasaki, K. Funatsu, 1988 .

Powerful Integrative Tool Combining Structure Generator and Chemical Space Visualization

K. Funatsu, K. Hasegawa, 2012 .

Representation of Molecular Configurations by CAST Coding Method.

K. Funatsu, H. Koshino, T. Nakata, 2001 .

Classification and Prediction of Reagents' Roles by FRAU System with Self-Organizing Neural Network Model

K. Funatsu, K. Takano, T. Nakata, 2000 .

Representation of Molecular Configurations by CAST Coding Method

Kimito Funatsu, Hiroko Satoh, Tadashi Nakata, 2001, J. Chem. Inf. Comput. Sci..

Novel Canonical Coding Method for Representation of Three‐Dimensional Structures.

K. Funatsu, H. Koshino, T. Nakata, 2000 .

Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection

K. Funatsu, H. Kaneko, Y. Masuda, 2014 .

Automated structure elucidation system — CHEMICS

S. Sasaki, K. Funatsu, C. D. Carpio, 1986 .

Quantitative Prediction of Regioselectivity Toward Cytochrome P450/3A4 Using Machine Learning Approaches

Kimito Funatsu, Michio Koyama, K. Funatsu, 2010, Molecular informatics.

Herman Skolnik Award Symposium 2015 Honoring Jürgen Bajorath

P. Willett, A. Wassermann, V. Shanmugasundaram, 2015 .

A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2011 .

Prediction of ProteinProtein Interaction Pocket Using L‐Shaped PLS Approach and Its Visualizations by Generative Topographic Mapping

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2014, Molecular informatics.

Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

K. Funatsu, Kenichi Tanaka, Chia-Hsiu Chen, 2018, Journal of Fluorescence.

Nonlinear regression method with variable region selection and application to soft sensors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2011 .

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions

Kazuma Kaitoh, Masaaki Kotera, K. Funatsu, 2019, Molecular informatics.

Development of a New De Novo Design Algorithm for Exploring Chemical Space

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2014, Molecular informatics.

Statistical Approach to Constructing Predictive Models for Thermal Resistance Based on Operating Conditions

Hiromasa Kaneko, Kimito Funatsu, Hirofumi Inokuchi, 2012 .

Development of a Pharmacophore Modeling Method and its Application to Phosphodiesterase-4 Inhibitors

M. Shoda, K. Funatsu, M. Arakawa, 2010 .

Advanced PLS Techniques in Chemometrics and Their Applications to Molecular Design

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2011 .

Prediction of Membrane Resistance inNewly Constructed Membrane Bioreactor

K. Funatsu, H. Kaneko, Shingo Omata, 2016 .

Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors

K. Funatsu, H. Kaneko, 2013 .

Development of The New Regression Analysis Method Using Independent Component Analysis and Genetic Algorithm

K. Funatsu, H. Kaneko, M. Arakawa, 2006 .

Tailored scoring function of Trypsin–benzamidine complex using COMBINE descriptors and support vector regression

K. Funatsu, K. Hasegawa, M. Arakawa, 2008 .

Estimation of predictive accuracy of soft sensor models based on data density

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Strategy of Structure Generation within Applicability Domains with One-Class Support Vector Machine

K. Funatsu, H. Kaneko, 2015 .

Model for predicting transmembrane pressure jump for various membrane bioreactors

K. Funatsu, H. Kaneko, 2015 .

Detection of nonlinearity in soil property prediction models based on near-infrared spectroscopy

Matheus S. Escobar, K. Funatsu, H. Kaneko, 2017 .

Iterative optimization technology combined with wavelength selection based on excess absorption for a process analytical technology calibration–minimum approach

K. Funatsu, H. Kaneko, Shojiro Shibayama, 2016 .

Adaptive model and model selection for long-term transmembrane pressure prediction in membrane bioreactors

K. Funatsu, H. Kaneko, H. Oishi, 2015 .

Partial constrained least squares (PCLS) and application in soft sensor

K. Funatsu, Kai-Yi Zheng, 2018, Chemometrics and Intelligent Laboratory Systems.

Automatic perception of reactivity characteristics of molecular structures directed to the planning of organic synthesis

S. Sasaki, K. Funatsu, C. D. Carpio, 1988 .

Strategic parameter search method based on prediction errors and data density for efficient product design

K. Funatsu, H. Kaneko, Takuya Kishio, 2013 .

Advanced PLS Technique Focusing on Visualization and Chemical Interpretation - SOMPLS Analysis of Serine Protease Inhibitors -

K. Funatsu, K. Hasegawa, 2010 .

Analysis of Quality in Fruit by NIR Spectrum

K. Funatsu, M. Arakawa, Yosuke Yamashita, 2011 .

Application of novel molecular alignment method using Hopfield Neural Network to 3D-QSAR

K. Funatsu, K. Hasegawa, M. Arakawa, 2002 .

Analyzing compound activity records and promiscuity

K. Funatsu, 2019 .

Analysis of a transmembrane pressure (TMP) jump prediction model for preventing TMP jumps

K. Funatsu, H. Kaneko, 2015 .

Development of a Model Predicting Transmembrane Pressurein Membrane Bioreactors

K. Funatsu, H. Kaneko, 2011 .

Novel alignment method of small molecules using Hopfield Neural Network

Kimito Funatsu, Masamoto Arakawa, K. Funatsu, 2001 .

Improvement of Process State Recognition Performance by Noise Reduction with Smoothing Methods

K. Funatsu, H. Kaneko, 2017 .

Development of a New Regression Method Considering Predictability and Molecular Design of Amine Compounds for CO2 Separation and Recovery

K. Funatsu, H. Kaneko, K. Mishima, 2013 .

Development of a Strategic Parameter Search Method forEfficient Product Design

K. Funatsu, H. Kaneko, Takuya Kishio, 2013 .

A New Method for Mapping the Molecular Surface of a Protein Structure Using a Spherical Self‐Organizing Map

K. Funatsu, K. Hasegawa, 2012, Molecular informatics.

Development of Soft Sensor Methods Based on Wavelength Region Selection Methods

K. Funatsu, H. Kaneko, 2012 .

Development of the computer software

K. Funatsu, Yoshiro Yamada, Masamoto Arakawa, 2005 .

BIMODAL PARTIAL LEAST‐SQUARES APPROACH AND ITS APPLICATION TO CHEMOGENOMICS STUDIES FOR MOLECULAR DESIGN

K. Funatsu, K. Hasegawa, 2013 .

Technique of Augmenting Molecular Graph Data by Perturbating Hidden Features

K. Funatsu, Kenichi Tanaka, Takahiro Inoue, 2022, Molecular informatics.

Physiologically Based Pharmacokinetic Models Predicting Renal and Hepatic Concentrations of Industrial Chemicals after Virtual Oral Doses in Rats.

M. Shimizu, H. Yamazaki, K. Funatsu, 2020, Chemical research in toxicology.

Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives

K. Funatsu, M. Okuyama, Ikeda Yuko, 2021, Journal of Computer Chemistry, Japan -International Edition.

Nonlinear CoMFA using QPLS as a Novel 3D-QSAR Approach

K. Funatsu, K. Hasegawa, Toshiro Kimura, 1997 .

Search Pathways for Secondary Metabolites Based on Genome Analysis and Chemical Knowledge on Metabolism

K. Funatsu, T. Nishioka, 1998 .

Structure Optimization of Gas Separation Membrane Module

K. Funatsu, Takuro Nishimura, 2007 .

Development of the Total System ToMoCo for Molecular Design

K. Funatsu, Masamoto Arakawa, 2006 .

SOPHIA, a Knowledge Base-Guided Reaction Prediction System - Utilization of a Knowledge Base Derived from a Reaction Database

Kimito Funatsu, Hiroko Satoh, K. Funatsu, 1995, J. Chem. Inf. Comput. Sci..

A chemi-informatics study to infer experimental yield of SNAr reactions by using parameters obtained from molecular orbital calculations

K. Funatsu, K. Kanai, K. Hori, 2004 .

GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists

Kimito Funatsu, Kiyoshi Hasegawa, Yoshikatsu Miyashita, 1997, J. Chem. Inf. Comput. Sci..

Application of the GA-PLS method for Aromatic nucleophilic substitution reaction on Quinolone derivatives using result of molcular orbital calculation for transition states

K. Funatsu, K. Hori, K. Okano, 2001 .

Soft-Sensor Modeling for Semi-Batch Chemical Process Using Limited Number of Sampling

Kimito Funatsu, Shinichiro Aoshima, Tomoyuki Miyao, 2019, Journal of Computer Aided Chemistry.

Molecular centrality for synthetic design of convergent reactions

Kimito Funatsu, Hideho Okamoto, Tetsuhiko Takabatake, 2008 .

Development of Evaluation Model for Strategic Sites in Synthetic Route Design System AIPHOS

K. Funatsu, Tsutomu Matsumoto, T. Takabatake, 2008 .

Varying resonance demands in substituent effects. Acetolysis of neophyl p-bromobenzenesulfonates

K. Funatsu, M. Fujio, M. Mishima, 1987 .

Solvent Effects on the Solvolysis of Neophyl Tosylates

M. Goto, K. Funatsu, Kenichi Yatsugi, 1992 .

3D-QSAR study of insecticidal neonicotinoid compounds based on 3-way partial least squares model

Kimito Funatsu, Masamoto Arakawa, K. Funatsu, 1999 .

Governing Factors for Carbon Nanotube Dispersion in Organic Solvents Estimated by Machine Learning

K. Funatsu, Tomoyuki Miyao, Y. Nonoguchi, 2022, Advanced Materials Interfaces.

Development of An Adaptive Soft Sensor Method Considering Prediction Confidence of Models

K. Funatsu, H. Kaneko, Takeshi Okada, 2012 .

Development of a model selection method based on the reliability of a soft sensor model

Hiromasa Kaneko, Kimito Funatsu, Takeshi Okada, 2012 .

Development of a new soft sensor method using independent component analysis and partial least squares

Hiromasa Kaneko, Kimito Funatsu, Masamoto Arakawa, 2009 .

Proposition of a New Fault Detection Method Using Independent Component Analysis and Support Vector Machine for Developing of High Predictive Soft Sensor

Hiromasa Kaneko, Kimito Funatsu, Masamoto Arakawa, 2009 .

Development of a New Regression Analysis Method Using Independent Component Analysis

Hiromasa Kaneko, Kimito Funatsu, Masamoto Arakawa, 2008, J. Chem. Inf. Model..

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Kenichi Tanaka, Kimito Funatsu, Masaaki Kotera, 2020, Journal of Cheminformatics.

Ensemble Machine Learning and Applicability Domain Estimation for Fluorescence Properties and its Application to Structural Design

Kimito Funatsu, Masaaki Kotera, Yuki Sugawara, 2019, Journal of Computer Aided Chemistry.

Substituent Effects. XVI. Acetolysis of 2-Phenylethyl Tosylates

K. Funatsu, M. Fujio, Y. Seki, 1987 .

Substituent effect on the acetolysis of 2-phenylethyl tosylate

K. Funatsu, M. Fujio, Y. Seki, 1983 .

Core Electron Binding Energy (CEBE) as Descriptors in Quantitative Structure - Activity Relationship (QSAR) Analysis of Cytotoxicities of a Series of Simple Phenols

K. Funatsu, Y. Takahata, Maximiliano Segala, 2007 .

An Automatic Modeling System of the Calculation Process of a CVD Film Deposition Simulator

K. Funatsu, Masamoto Arakawa, Takahiro Takahashi, 2010 .

An Autonomous and Intelligent System Using Mobile-Agent Software to Model the Calculation Processes of Film Deposition Simulators

K. Funatsu, Masamoto Arakawa, Takahiro Takahashi, 2007 .

Automatic modelling of reaction systems using genetic algorithms and its application to chemical vapour deposition processes: advanced utilizations of simulators for chemical systems

Kimito Funatsu, Yoshinori Ema, K. Funatsu, 2004 .

Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x)

Hiromasa Kaneko, Kimito Funatsu, Tomoyuki Miyao, 2016, J. Chem. Inf. Model..

Discrimination of poly(vinyl chloride) samples with different plasticizers and prediction of plasticizer contents in poly(vinyl chloride) using near-infrared spectroscopy and neural-network analysis.

K. Funatsu, K. Tanabe, Kazumitsu Saeki, 2003, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

Fast optimization of hyperparameters for support vector regression models with highly predictive ability

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2015 .

2D and 3D QSAR studies of the receptor binding affinity of progestins

K. Funatsu, L. Veras, Y. Takahata, 2010 .

Identification of Bioactive Scaffolds Based on QSAR Models

Kimito Funatsu, Tomoyuki Miyao, K. Funatsu, 2018, Molecular informatics.

A new process variable and dynamics selection method based on a genetic algorithm‐based wavelength selection method

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2012 .

GA strategy for variable selection in QSAR studies: GAPLS and D-optimal designs for predictive QSAR model

K. Funatsu, K. Funatsu, K. Hasegawa, 1998 .

Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets

K. Funatsu, Shojiro Shibayama, 2021, AAPS PharmSciTech.

Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets

K. Funatsu, Shojiro Shibayama, 2021, AAPS PharmSciTech.

Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013, J. Chem. Inf. Model..

Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping

Kimito Funatsu, Tomoyuki Miyao, Hiroshi Nakano, 2021, J. Chem. Inf. Model..

Development of system for selecting optimal zeolite–solvent combination for liquid chromatography: I. Constructing a database of solute sorption amounts

K. Funatsu, Toshiaki Kusaba, Miyamoto Shinji, 2012 .

Fast calculation of π-electron charge densities in organic molecules: Non-condensed compounds

S. Sasaki, K. Funatsu, I. Bangov, 1995 .

Determination and prediction of permeability across intestinal epithelial cell monolayer of a diverse range of industrial chemicals/drugs for estimation of oral absorption as a putative marker of hepatotoxicity

M. Shimizu, H. Yamazaki, K. Funatsu, 2020, Toxicology reports.

Plasma and Hepatic Concentrations of Chemicals after Virtual Oral Administrations Extrapolated Using Rat Plasma Data and Simple Physiologically Based Pharmacokinetic Models.

H. Yamazaki, K. Funatsu, Tomonori Miura, 2018, Chemical research in toxicology.

Construction of a Statistical Evaluation Model Based on Molecular Centrality to Find Retrosynthetically Important Bonds in Organic Compounds

Kimito Funatsu, Hideho Okamoto, Tetsuhiko Takabatake, 2008 .

Ranking-Oriented Quantitative Structure–Activity Relationship Modeling Combined with Assay-Wise Data Integration

K. Funatsu, Tomoyuki Miyao, Katsuhisa Matsumoto, 2021, ACS omega.

GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors

Kimito Funatsu, Kiyoshi Hasegawa, Toshiro Kimura, 1999, J. Chem. Inf. Comput. Sci..

GA Strategy for Variable Selection in QSAR Studies: GA-Based Region Selection for CoMFA Modeling

Kimito Funatsu, Kiyoshi Hasegawa, Toshiro Kimura, 1998, J. Chem. Inf. Comput. Sci..

Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel

Swarit Jasial, K. Funatsu, Tomoyuki Miyao, 2021, Molecules.

Evolution of PLS for Modeling SAR and omics Data

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2012, Molecular informatics.

On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification

Hiromasa Kaneko, Kimito Funatsu, Matheus S. Escobar, 2017, Comput. Chem. Eng..

Data Visualization & Clustering: Generative Topographic Mapping Similarity Assessment Allied to Graph Theory Clustering

Hiromasa Kaneko, Kimito Funatsu, Matheus S. Escobar, 2016 .

Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2015 .

Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification

Hiromasa Kaneko, Kimito Funatsu, Matheus S. Escobar, 2015 .

GA Strategy for Variable Selection in QSAR Studies: Enhancement of Comparative Molecular Binding Energy Analysis by GA‐Based PLS Method

Kimito Funatsu, K. Funatsu, K. Hasegawa, 1999 .

An Updated In Silico Prediction Method for Volumes of Systemic Circulation of 323 Disparate Chemicals for Use in Physiologically Based Pharmacokinetic Models to Estimate Plasma and Tissue Concentrations after Oral Doses in Rats.

M. Shimizu, K. Funatsu, Tomonori Miura, 2021, Chemical research in toxicology.

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

K. Funatsu, Yusuke Kamiya, H. Yamazaki, 2021, Biochemical pharmacology.

In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals.

M. Shimizu, H. Yamazaki, K. Funatsu, 2021, Chemical research in toxicology.

Computer-assisted structure/taste studies on sulfamates by pattern recognition methods

Kimito Funatsu, Yoshimasa Takahashi, Shin-ichi Sasaki, 1986 .

Introduction of NOE data to an automated structure elucidation system, CHEMICS. Three-dimensional structure elucidation using the distance geometry method

Kimito Funatsu, Shin-ichi Sasaki, Minoru Nishizaki, 1994, J. Chem. Inf. Comput. Sci..

Exploring differential evolution for inverse QSAR analysis [version 2; peer review: 3 approved]

J. Bajorath, K. Funatsu, Tomoyuki Miyao, 2021 .

Practical Models for Predicting the Emission Peak Wavelengths of Inorganic Phosphors Based on Stoichiometric Information

K. Funatsu, H. Nakano, Tomoyuki Miyao, 2017 .

Further development of structure generation in the automated structure elucidation system CHEMICS

Kimito Funatsu, Nobuyoshi Miyabayashi, Shinichi Sasaki, 1988, J. Chem. Inf. Comput. Sci..

Flour concentration prediction using GAPLS and GAWLS focused on data sampling issues and applicability domain

Hiromasa Kaneko, Kimito Funatsu, Matheus S. Escobar, 2014 .

New Molecular Surface-based 3D-QSAR Method using Kohonen Neural Network and 3-way PLS

Kimito Funatsu, Kiyoshi Hasegawa, Masamoto Arakawa, 2002, Comput. Chem..

Partial Least Squares Modeling and Genetic Algorithm Optimization in Quantitative Structure-Activity Relationships

K Hasegawa, K Funatsu, K. Funatsu, 2000, SAR and QSAR in environmental research.

Optimization of the Inner Relation Function of QPLS Using Genetic Algorithm

Hiroshi Yoshida, Kimito Funatsu, K. Funatsu, 1997, J. Chem. Inf. Comput. Sci..

Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

K. Funatsu, Kenichi Tanaka, Chia-Hsiu Chen, 2018, Journal of Fluorescence.

Applicability Domains and Consistent Structure Generation

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2017, Molecular informatics.

Non-linear modeling and chemical interpretation with aid of support vector machine and regression.

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2010, Current computer-aided drug design.

Development of Drug-likeness Model and Its Visualization

Kimito Funatsu, Tomoyuki Miyao, Masamoto Arakawa, 2008 .

Novel Canonical Coding Method for Representation of Three-Dimensional Structures

Kimito Funatsu, Hiroko Satoh, Tadashi Nakata, 2000, J. Chem. Inf. Comput. Sci..

Classification of drug tablets using hyperspectral imaging and wavelength selection with a GAWLS method modified for classification.

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2015, International journal of pharmaceutics.

Physical and statistical model for predicting a transmembrane pressure jump for a membrane bioreactor

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Visualization of Models Predicting Transmembrane Pressure Jump for Membrane Bioreactor

K. Funatsu, H. Kaneko, 2012 .

Construction of Long-Term Transmembrane Pressure Estimation Model for a Membrane Bioreactor

K. Funatsu, H. Kaneko, Kyung-mo Sung, 2012 .

Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties

Kimito Funatsu, Tomoyuki Miyao, K. Funatsu, 2019, J. Chem. Inf. Model..

Systematic generation of chemical structures for rational drug design based on QSAR models.

Kimito Funatsu, Tomoyuki Miyao, Masamoto Arakawa, 2011, Current computer-aided drug design.

Application of Online Support Vector Regression for Soft Sensors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2014 .

Design Optimization of Photoelectrode Reactor for Hydrogen Production by Combining CFD Simulation and Statistical Methods

K. Funatsu, Y. Maeda, 2011 .

Simultaneous determination of bioactive conformations and alignment rules by multi-way PLS modeling

Kimito Funatsu, Kiyoshi Hasegawa, Masamoto Arakawa, 2003, Comput. Biol. Chem..

Rational choice of bioactive conformations through use of conformation analysis and 3-way partial least squares modeling

Kimito Funatsu, Masamoto Arakawa, K. Funatsu, 2000 .

Development of drug-likeness model using generative topographic mapping

Kimito Funatsu, Tomoyuki Miyao, Hiroshi Aoki, 2007 .

Introduction of two-dimensional NMR spectral information to an automated structure elucidation system, CHEMICS. Utilization of 2D-INADEQUATE information

Kimito Funatsu, Shin-ichi Sasaki, Yutaka Susuta, 1989, J. Chem. Inf. Comput. Sci..

Recent Advances in the Automated Structure Elucidation System, CHEMICS. Utilization of Two-Dimensional NMR Spectral Information and Development of Peripheral Functions for Examination of Candidates

K. Funatsu, S. Sasaki, K. Funatsu, 1994, J. Chem. Inf. Comput. Sci..

Introduction of Two-Dimensional NMR Spectral Information to an Automated Structure Elucidation System, CHEMICS. Utilization of 2D-INADEQUATE Information.

S. Sasaki, K. Funatsu, Yutaka Susuta, 1989 .

QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network

Kimito Funatsu, Masamoto Arakawa, K. Funatsu, 2006 .

Classification of Organic Reactions: Similarity of Reactions Based on Changes in the Electronic Features of Oxygen Atoms at the Reaction Sites1

Kimito Funatsu, Hiroko Satoh, Tadashi Nakata, 1998, Journal of chemical information and computer sciences.

Multi-way PLS modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface

Kimito Funatsu, Kiyoshi Hasegawa, Masamoto Arakawa, 2003, Comput. Biol. Chem..

A Novel Method for Characterization of Three-Dimensional Reaction Fields Based on Electrostatic and Steric Interactions toward the Goal of Quantitative Analysis and Understanding of Organic Reactions

Kimito Funatsu, Hiroko Satoh, Tadashi Nakata, 1999, J. Chem. Inf. Comput. Sci..

Application of the Novel Molecular Alignment Method Using the Hopfield Neural Network to 3D-QSAR

Kimito Funatsu, Kiyoshi Hasegawa, Masamoto Arakawa, 2003, J. Chem. Inf. Comput. Sci..

A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture

Hiromasa Kaneko, Kimito Funatsu, Shojiro Shibayama, 2016, AAPS PharmSciTech.

Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring

Hiromasa Kaneko, Kimito Funatsu, Shojiro Shibayama, 2018, Comput. Chem. Eng..

Improvement of iterative optimization technology (for process analytical technology calibration-free/minimum approach) with dimensionality reduction and wavelength selection of spectra

Hiromasa Kaneko, Kimito Funatsu, Koji Muteki, 2015 .

Applicability domains and accuracy of prediction of soft sensor models

Hiromasa Kaneko, Kimito Funatsu, Masamoto Arakawa, 2011 .

Process Control and Soft Sensors

Kimito Funatsu, K. Funatsu, 2018 .

Genetic algorithm‐based wavelength selection method for spectral calibration

Kimito Funatsu, Masamoto Arakawa, Yosuke Yamashita, 2011 .

Metabolic disassembler for understanding and predicting the biosynthetic units of natural products

Kenichi Tanaka, Kimito Funatsu, Masaaki Kotera, 2019, BMC Bioinformatics.

Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces

Hiromasa Kaneko, Kimito Funatsu, Iwao Maeda, 2017, Molecular informatics.

Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2011 .

Computer-Assisted Structure Elucidation for Organic Compounds

K. Funatsu, 1993 .

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules

Hiromasa Kaneko, Kimito Funatsu, Shunichi Takeda, 2016, J. Chem. Inf. Model..

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning

K. Funatsu, Tomoyuki Miyao, Akinori Sato, 2021, Molecular informatics.

Generative topographic mapping of binding pocket of β2 receptor and three‐way partial least squares modeling of inhibitory activities

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2014 .

New description of protein-ligand interactions using a spherical self-organizing map.

Kimito Funatsu, K. Funatsu, K. Hasegawa, 2012, Bioorganic & medicinal chemistry.

Development of Multi-Dimensional Data-Modeling Software

K. Funatsu, Masamoto Arakawa, 2004 .

Proposal of New Catalysts Using Chemoinformatics Method

K. Funatsu, Masamoto Arakawa, Uemura Keisuke, 2006 .

Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations

Kimito Funatsu, Tomoyuki Miyao, Swarit Jasial, 2021, Journal of Computer-Aided Molecular Design.

Database monitoring index for adaptive soft sensors and the application to industrial process

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2014 .

Automatic Determination Method Based on Cross-Validation for Optimal Intervals of Time Difference

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Soft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants

Kimito Funatsu, K. Funatsu, 2016, Molecular informatics.

Moving Window and Just-in-Time Soft Sensor Model Based on Time Differences Considering a Small Number of Measurements

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2015 .

Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013, Comput. Chem. Eng..

Maintenance-free soft sensor models with time difference of process variables

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2011 .

Consideration of Soft Sensor Methods Based on Time Difference and Discussion on Intervals of Time Difference

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2012 .

Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure

Kimito Funatsu, Tomoyuki Miyao, Shunsuke Tamura, 2019, J. Chem. Inf. Model..

A data-driven scheme to search for alternative composite materials

K. Funatsu, Y. Nakazawa, Michihiro Okuyama, 2022, Science and Technology of Advanced Materials: Methods.

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space

Kimito Funatsu, Tomoyuki Miyao, K. Funatsu, 2017, Molecular informatics.

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

Kimito Funatsu, Tomoyuki Miyao, Hiroshi Nakano, 2020, J. Chem. Inf. Model..

Nonlinear Partial Least Squares Modeling of Phenyl Alkylamines with the Monoamine Oxidase Inhibitory Activities

Kimito Funatsu, Kiyoshi Hasegawa, Toshiro Kimura, 1996, J. Chem. Inf. Comput. Sci..

Application of orthogonal L-shaped PLS to chemogenomics data and its chemical interpretation from predictive and orthogonal regression coefficients

K. Funatsu, K. Hasegawa, 2014 .

Analyzing compound activity records and promiscuity degrees

K. Funatsu, 2020 .

De novo Drug Design – Ye olde Scoring Problem Revisited

Gisbert Schneider, Kimito Funatsu, Yasushi Okuno, 2017, Molecular informatics.

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x)

K. Funatsu, Tomoyuki Miyao, H. Kaneko, 2016, J. Chem. Inf. Model..

Chemography of Natural Product Space

Petra Schneider, Gisbert Schneider, Kimito Funatsu, 2015, Planta Medica.

Ligand‐based Activity Cliff Prediction Models with Applicability Domain

Kimito Funatsu, Tomoyuki Miyao, Shunsuke Tamura, 2020, Molecular informatics.

Exhaustive Structure Generation for Inverse‐QSPR/QSAR

K. Funatsu, Tomoyuki Miyao, Masamoto Arakawa, 2010, Molecular informatics.

Preparation of comprehensive data from huge data sets for predictive soft sensors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2016 .

Random Forest Model with Combined Features: A Practical Approach to Predict Liquid‐crystalline Property

Kimito Funatsu, Kenichi Tanaka, K. Funatsu, 2018, Molecular informatics.

Prediction of Polyethylene Density by Near-Infrared Spectroscopy Combined with Neural Network Analysis

K. Funatsu, Takatoshi Matsumoto, K. Tanabe, 2003 .

Ring‐System‐Based Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Hiromasa Kaneko, Kimito Funatsu, Tomoyuki Miyao, 2014, Molecular informatics.

Dry Etching Damage and Alloy Composition Analysis of GaN-Based Semiconductors Using Electron Energy-Loss Spectroscopy

Masaaki Kotera, K. Funatsu, H. Nakano, 2021, Journal of Electronic Materials.

Soft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants

K. Funatsu, 2016, Molecular informatics.

Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation

K. Funatsu, H. Kaneko, 2013, J. Chem. Inf. Model..

Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2014 .

Practical Use of Savitzky-Golay Filtering-Based Ensemble Online SVR

Kimito Funatsu, Hiromasa Kaneko, Takuya Matsumoto, 2016 .

Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2015 .

Selective Use of Adaptive Soft Sensors Based on Process State

Hiromasa Kaneko, Kimito Funatsu, Takeshi Okada, 2014 .

Automatic Database Monitoring for Process Control Systems

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2014, IEA/AIE.

A chemometric approach to prediction of transmembrane pressure in membrane bioreactors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Novel soft sensor method for detecting completion of transition in industrial polymer processes

Hiromasa Kaneko, Kimito Funatsu, Masamoto Arakawa, 2011, Comput. Chem. Eng..

Development of a New Feed-Forward Control Method Based on Soft Sensors and Inverse Analysis

K. Funatsu, H. Kaneko, I. Kimura, 2015 .

Development of a New Index to Monitor Database for Soft Sensors

Hiromasa Kaneko, Kimito Funatsu, K. Funatsu, 2013 .

Prediction Models for Soil Properties Using VIS-NIR Spectroscopy

K. Funatsu, Masaya Ando, Masamoto Arakawa, 2009 .

Development of genetic algorithm-based wavelength regional selection technique

K. Funatsu, Masamoto Arakawa, S. Kawamura, 2006 .

Improvement of Prediction Accuracy in Just-In-Time Modelling Using Distance-based Database Update

Kimito Funatsu, Hiromasa Kaneko, Kenichi Tanaka, 2015 .

Quantitative Structure−Activity Relationships of the Synthetic Substrates for Elastase Enzyme Using Nonlinear Partial Least Squares Regression

Kimito Funatsu, Shin-ichi Sasaki, Toshiro Kimura, 1996 .

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships

Jürgen Bajorath, Kimito Funatsu, Tomoyuki Miyao, 2019, Journal of Computer-Aided Molecular Design.

Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models.

Swarit Jasial, K. Funatsu, Tomoyuki Miyao, 2022, ACS pharmacology & translational science.

Knowledge-Oriented Applications in Data Mining

Kimito Funatsu, K. Funatsu, 2011 .

Classification of Organic Reactions: Similarity of Reactions Based on Changes in the Electronic Features of Oxygen Atoms at the Reaction Sites1

Johann Gasteiger, William Lingran Chen, Kimito Funatsu, 1998, J. Chem. Inf. Comput. Sci..

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Jürgen Bajorath, Kimito Funatsu, Tomoyuki Miyao, 2018, J. Chem. Inf. Model..