Kei Terayama
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ChemTS: an efficient python library for de novo molecular generation
X Yang, J Zhang, K Yoshizoe, K Terayama, K Tsuda
Science and technology of advanced materials 18 (1), 972-976, 2017
Population-based de novo molecule generation, using grammatical evolution
N Yoshikawa, K Terayama, M Sumita, T Homma, K Oono, K Tsuda
Chemistry Letters 47 (11), 1431-1434, 2018
Black-Box Optimization for Automated Discovery
K Terayama, M Sumita, R Tamura, K Tsuda
Accounts of Chemical Research 54 (6), 1334-1346, 2021
Extraction of protein dynamics information from cryo-EM maps using deep learning
S Matsumoto, S Ishida, M Araki, T Kato, K Terayama, Y Okuno
Nature Machine Intelligence 3 (2), 153-160, 2021
Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks
S Ishida, K Terayama, R Kojima, K Takasu, Y Okuno
Journal of chemical information and modeling 59 (12), 5026-5033, 2019
Integration of sonar and optical camera images using deep neural network for fish monitoring
K Terayama, K Shin, K Mizuno, K Tsuda
Aquacultural Engineering 86, 102000, 2019
Deep-learning-based quality filtering of mechanically exfoliated 2D crystals
Y Saito, K Shin, K Terayama, S Desai, M Onga, Y Nakagawa, YM Itahashi, ...
npj Computational Materials 5 (1), 124, 2019
Efficient construction method for phase diagrams using uncertainty sampling
K Terayama, R Tamura, Y Nose, H Hiramatsu, H Hosono, Y Okuno, ...
Physical Review Materials 3 (3), 033802, 2019
Bayesian optimization package: PHYSBO
Y Motoyama, R Tamura, K Yoshimi, K Terayama, T Ueno, K Tsuda
Computer Physics Communications 278, 108405, 2022
Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations
B Ma, K Terayama, S Matsumoto, Y Isaka, Y Sasakura, H Iwata, M Araki, ...
Journal of Chemical Information and Modeling 61 (7), 3304-3313, 2021
NMR-TS: de novo molecule identification from NMR spectra
J Zhang, K Terayama, M Sumita, K Yoshizoe, K Ito, J Kikuchi, K Tsuda
Science and Technology of Advanced Materials 21 (1), 552-561, 2020
CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration
R Shibukawa, S Ishida, K Yoshizoe, K Wasa, K Takasu, Y Okuno, ...
Journal of cheminformatics 12 (1), 1-14, 2020
Pushing property limits in materials discovery via boundless objective-free exploration
K Terayama, M Sumita, R Tamura, DT Payne, MK Chahal, S Ishihara, ...
Chemical science 11 (23), 5959-5968, 2020
Machine learning accelerates MD-based binding pose prediction between ligands and proteins
K Terayama, H Iwata, M Araki, Y Okuno, K Tsuda
Bioinformatics 34 (5), 770-778, 2018
Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method
K Shin, DP Tran, K Takemura, A Kitao, K Terayama, K Tsuda
ACS omega 4 (9), 13853-13862, 2019
A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens
S Nojima, K Terayama, S Shimoura, S Hijiki, N Nonomura, E Morii, ...
Cancer Cytopathology 129 (12), 984-995, 2021
Fine-grained optimization method for crystal structure prediction
K Terayama, T Yamashita, T Oguchi, K Tsuda
npj Computational Materials 4 (1), 32, 2018
Efficient recommendation tool of materials by an executable file based on machine learning
K Terayama, K Tsuda, R Tamura
Japanese Journal of Applied Physics 58 (9), 098001, 2019
De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning
M Sumita, K Terayama, N Suzuki, S Ishihara, R Tamura, MK Chahal, ...
Science advances 8 (10), eabj3906, 2022
Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis
Y Zhang, J Zhang, K Suzuki, M Sumita, K Terayama, J Li, Z Mao, K Tsuda, ...
Applied Physics Letters 118 (22), 223904, 2021
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