Kei Terayama

发表

Efficient construction method for phase diagrams using uncertainty sampling

H. Hosono, K. Tsuda, Y. Nose, 2018, Physical Review Materials.

NMR-TS: de novo molecule identification from NMR spectra

K. Tsuda, Kazuki Yoshizoe, Kengo Ito, 2020, Science and technology of advanced materials.

Bayesian optimization package: PHYSBO

Tsuyoshi Ueno, K. Tsuda, Yuichi Motoyama, 2021, Comput. Phys. Commun..

Individual health-disease phase diagrams for disease prevention based on machine learning

Y. Tamada, R. Kojima, Eiichiro Uchino, 2022, ArXiv.

Individual health-disease phase diagrams for disease prevention based on machine learning

Y. Tamada, R. Kojima, Eiichiro Uchino, 2022, Journal of biomedical informatics.

Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks

R. Kojima, Kei Terayama, Yasushi Okuno, 2019, J. Chem. Inf. Model..

ORIGINAL A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathologic Diagnosis of In ﬂ ammatory Bowel Diseases

E. Susaki, H. Ueda, Y. Doki, 2022 .

NMR-TS: de novo molecule identification from NMR spectra

K. Tsuda, Kazuki Yoshizoe, Kengo Ito, 2020, Science and technology of advanced materials.

Topological alternation from structurally adaptable to mechanically stable crosslinked polymer

I. Watanabe, Kei Terayama, Ryo Tamura, 2022, Science and technology of advanced materials.

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration

K. Tsuda, Kazuki Yoshizoe, Kei Terayama, 2020, Journal of Cheminformatics.

A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathological Diagnosis of Inflammatory Bowel Diseases

E. Susaki, H. Ueda, Y. Doki, 2022, Cellular and molecular gastroenterology and hepatology.

De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning

K. Tsuda, Kazuki Yoshizoe, Shinsuke Ishihara, 2022, Science advances.

Looking represents choosing in toddlers: Exploring the equivalence between multimodal measures in forced-choice tasks.

Y. Moriguchi, Kei Terayama, M. Sakagami, 2020, Infancy : the official journal of the International Society on Infant Studies.

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Y. Hayashi, Kei Terayama, Yasushi Okuno, 2022, International journal of pharmaceutics: X.

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search

T. Honma, Tomohiro Sato, Kei Terayama, 2022, J. Chem. Inf. Model..

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring

K. Tsuda, M. Sumita, Kei Terayama, 2022, Science and technology of advanced materials.

Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications

Kei Terayama, Naoto Ienaga, Hiromichi Hagihara, 2022, Occupational therapy international.

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

K. Tsuda, M. Sumita, Kei Terayama, 2022, J. Chem. Inf. Model..

Integrating Incompatible Assay Data Sets with Deep Preference Learning

K. Tsuda, K. Mori, M. Sumita, 2021, ACS Medicinal Chemistry Letters.

Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters

Yoshifumi Amamoto, A. Takahara, K. Kojio, 2021, Polymer Journal.

A machine learning–based classification approach for phase diagram prediction

Kei Terayama, Ryo Tamura, G. Deffrennes, 2022, Materials & Design.

Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network

K. Tsuda, Kei Terayama, Toshinori Takashi, 2021, Scientific Reports.

CrySPY: a crystal structure prediction tool accelerated by machine learning

Tomoki Yamashita, Hiori Kino, Tamio Oguchi, 2021, Science and Technology of Advanced Materials: Methods.

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Koji Tsuda, Yasushi Okuno, Ryo Kanada, 2020, Biomolecules.

Extraction of protein dynamics information from cryo-EM maps using deep learning

Yasushi Okuno, Mitsugu Araki, Takayuki Kato, 2021, Nature Machine Intelligence.

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations

Yasushi Okuno, Mitsugu Araki, Kei Terayama, 2021, J. Chem. Inf. Model..

Pushing property limits in materials discovery via boundless objective-free exploration† †Electronic supplementary information (ESI) available: The details of BLOX and experimental spectroscopic data. See DOI: 10.1039/d0sc00982b

Shinsuke Ishihara, Kei Terayama, Daniel T. Payne, 2020, Chemical science.

Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization.

K. Tsuda, M. Sumita, Michio Katouda, 2021, Journal of chemical theory and computation.

Extraction of Protein Dynamics Information Hidden in Cryogenic Electron Microscopy Maps Using Deep Learning

Yasushi Okuno, Mitsugu Araki, Kei Terayama, 2020, bioRxiv.

Deep-learning-based quality filtering of mechanically exfoliated 2D crystals

Makoto Yamada, Kei Terayama, Koji Tsuda, 2019, npj Computational Materials.

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge

R. Kojima, Kei Terayama, Yasushi Okuno, 2022, J. Chem. Inf. Model..

Black-Box Optimization for Automated Discovery.

K. Tsuda, M. Sumita, Kei Terayama, 2021, Accounts of chemical research.

A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens

E. Morii, N. Nonomura, K. Fujita, 2021, Cancer cytopathology.

ChemTS: an efficient python library for de novo molecular generation

Koji Tsuda, Xiufeng Yang, Kazuki Yoshizoe, 2017, Science and technology of advanced materials.

Population-based de novo molecule generation, using grammatical evolution

Koji Tsuda, Kenta Oono, Teruki Honma, 2018, 1804.02134.

Computer Vision-Based Approach for Quantifying Occupational Therapists' Qualitative Evaluations of Postural Control

Koji Tsuda, Naoto Ienaga, Kei Terayama, 2020, Occupational therapy international.

Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network

Atsushi Tokuhisa, Yuji Okamoto, Kei Terayama, 2021, J. Chem. Inf. Model..

Coarse-Grained Diffraction Template Matching Model to Retrieve Multiconformational Models for Biomolecule Structures from Noisy Diffraction Patterns

Biao Ma, Yasushi Okuno, Kei Terayama, 2020, J. Chem. Inf. Model..

Automatic Rietveld refinement by robotic process automation with RIETAN-FP

K. Tsuda, Y. Matsushita, F. Izumi, 2022, Science and Technology of Advanced Materials: Methods.

Efficient recommendation tool of materials by an executable file based on machine learning

Koji Tsuda, Ryo Tamura, Kei Terayama, 2019, Japanese Journal of Applied Physics.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics

Atsushi Tokuhisa, R. Kanada, Kei Terayama, 2022, Journal of chemical theory and computation.

A framework to predict binary liquidus by combining machine learning and CALPHAD assessments

Kei Terayama, Ryo Tamura, G. Deffrennes, 2023, Materials & Design.

Experimental Establishment of Phase Diagrams Guided by Uncertainty Sampling: An Application to the Deposition of Zn–Sn–P Films by Molecular Beam Epitaxy

Y. Nose, Kei Terayama, Ryo Tamura, 2020 .

Fine-grained optimization method for crystal structure prediction

Koji Tsuda, Tomoki Yamashita, Kei Terayama, 2018, npj Computational Materials.

Deep-learning-based differential diagnosis of follicular thyroid tumors using histopathological images.

M. Hirokawa, E. Morii, K. Fujita, 2023, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc.

Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach

Koji Tsuda, Biao Ma, Hiroaki Iwata, 2018, J. Comput. Chem..

Integration of sonar and optical camera images using deep neural network for fish monitoring

Koji Tsuda, Katsunori Mizuno, Kei Terayama, 2019, Aquacultural Engineering.

Machine learning accelerates MD-based binding pose prediction between ligands and proteins

Koji Tsuda, Hiroaki Iwata, Yasushi Okuno, 2017, Bioinform..

AI-Driven Synthetic Route Design with Retrosynthesis Knowledge

R. Kojima, Kei Terayama, Yasushi Okuno, 2020 .

evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

Koji Tsuda, Akio Kitao, Kei Terayama, 2019, The Journal of chemical physics.

ChemTSv2: Functional molecular design using de novo molecule generator

Kazuki Yoshizoe, M. Sumita, Michio Katouda, 2023, WIREs Computational Molecular Science.

ChemTSv2: Democratizing Functional Molecular Design Using de novo Molecule Generator

Kazuki Yoshizoe, Kei Terayama, Shoichi Ishida, 2023 .

Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis

M. Sumita, Kuniko Suzuki, Kei Terayama, 2021 .

Integrating Incompatible Assay Data Sets with Deep Preference Learning

K. Mori, M. Sumita, Kei Terayama, 2021, ACS medicinal chemistry letters.

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules.

Kei Terayama, Ryo Tamura, Takehiro Fujita, 2023, Journal of chemical theory and computation.

Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system

Akira Takahashi, Kei Terayama, Ryo Tamura, 2023, Science and Technology of Advanced Materials: Methods.

Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method

Koji Tsuda, Akio Kitao, Kei Terayama, 2019, ACS omega.

Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule

Y. Nose, Kei Terayama, Ryo Tamura, 2022, Scripta Materialia.

Predicting condensate formation of protein and RNA under various environmental conditions

Kei Terayama, Shoichi Ishida, Chin Ka Yin, 2023, bioRxiv.