| 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 | 287 | 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 | 131 | 2018 |
| Black-Box Optimization for Automated Discovery K Terayama, M Sumita, R Tamura, K Tsuda Accounts of Chemical Research 54 (6), 1334-1346, 2021 | 120 | 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 | 80 | 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 | 76 | 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 | 63 | 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 | 60 | 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 | 53 | 2019 |
| Bayesian optimization package: PHYSBO Y Motoyama, R Tamura, K Yoshimi, K Terayama, T Ueno, K Tsuda Computer Physics Communications 278, 108405, 2022 | 51 | 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 | 49 | 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 | 43 | 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 | 36 | 2020 |
| CrySPY: a crystal structure prediction tool accelerated by machine learning T Yamashita, S Kanehira, N Sato, H Kino, K Terayama, H Sawahata, ... Science and Technology of Advanced Materials: Methods 1 (1), 87-97, 2021 | 35 | 2021 |
| 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 | 34 | 2021 |
| 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, 1-14, 2020 | 33 | 2020 |
| 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 | 32 | 2022 |
| 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 | 32 | 2019 |
| 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 | 31 | 2018 |
| AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge S Ishida, K Terayama, R Kojima, K Takasu, Y Okuno Journal of chemical information and modeling 62 (6), 1357-1367, 2022 | 30 | 2022 |
| 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 | 28 | 2019 |
Koji TsudaProfessor, GSFS, The University of TokyoDirección de correo verificada de k.u-tokyo.ac.jp