| Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x) T Miyao, H Kaneko, K Funatsu Journal of chemical information and modeling 56 (2), 286-299, 2016 | 119 | 2016 |
| Exhaustive structure generation for inverse‐QSPR/QSAR T Miyao, M Arakawa, K Funatsu Molecular informatics 29 (1‐2), 111-125, 2010 | 60 | 2010 |
| Prediction of compound profiling matrices using machine learning R Rodríguez-Pérez, T Miyao, S Jasial, M Vogt, J Bajorath ACS omega 3 (4), 4713-4723, 2018 | 39 | 2018 |
| Systematic generation of chemical structures for rational drug design based on QSAR models K Funatsu, T Miyao, M Arakawa Current Computer-Aided Drug Design 7 (1), 1-9, 2011 | 37 | 2011 |
| Ring‐System‐Based Exhaustive Structure Generation for Inverse‐QSPR/QSAR T Miyao, H Kaneko, K Funatsu Molecular informatics 33 (11‐12), 764-778, 2014 | 27 | 2014 |
| Chemography of natural product space T Miyao, D Reker, P Schneider, K Funatsu, G Schneider Planta medica 81 (06), 429-435, 2015 | 24 | 2015 |
| Ring system-based chemical graph generation for de novo molecular design T Miyao, H Kaneko, K Funatsu Journal of computer-aided molecular design 30, 425-446, 2016 | 20 | 2016 |
| Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations A Sato, T Miyao, S Jasial, K Funatsu Journal of Computer-Aided Molecular Design 35, 179-193, 2021 | 16 | 2021 |
| Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning A Sato, T Miyao, K Funatsu Molecular Informatics 41 (2), 2100156, 2022 | 15 | 2022 |
| Soft sensor modeling for identifying significant process variables with time delays T Hikosaka, S Aoshima, T Miyao, K Funatsu Industrial & Engineering Chemistry Research 59 (26), 12156-12163, 2020 | 14 | 2020 |
| Extended connectivity fingerprints as a chemical reaction representation for enantioselective organophosphorus-catalyzed asymmetric reaction prediction R Asahara, T Miyao ACS omega 7 (30), 26952-26964, 2022 | 13 | 2022 |
| Exploring differential evolution for inverse QSAR analysis T Miyao, K Funatsu, J Bajorath F1000Research 6, 2017 | 13 | 2017 |
| Exploring topological pharmacophore graphs for scaffold hopping H Nakano, T Miyao, K Funatsu Journal of Chemical Information and Modeling 60 (4), 2073-2081, 2020 | 11 | 2020 |
| Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity S Tamura, T Miyao, J Bajorath Journal of Cheminformatics 15 (1), 4, 2023 | 10 | 2023 |
| Exploring alternative strategies for the identification of potent compounds using support vector machine and regression modeling T Miyao, K Funatsu, J Bajorath Journal of Chemical Information and Modeling 59 (3), 983-992, 2018 | 10 | 2018 |
| Computational method for estimating progression saturation of analog series R Kunimoto, T Miyao, J Bajorath RSC advances 8 (10), 5484-5492, 2018 | 10 | 2018 |
| Finding chemical structures corresponding to a set of coordinates in chemical descriptor space T Miyao, K Funatsu Molecular informatics 36 (8), 1700030, 2017 | 10 | 2017 |
| Sparse topological pharmacophore graphs for interpretable scaffold hopping H Nakano, T Miyao, J Swarit, K Funatsu Journal of Chemical Information and Modeling 61 (7), 3348-3360, 2021 | 9 | 2021 |
| Governing factors for carbon nanotube dispersion in organic solvents estimated by machine learning Y Nonoguchi, T Miyao, C Goto, T Kawai, K Funatsu Advanced Materials Interfaces 9 (7), 2101723, 2022 | 8 | 2022 |
| Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents O Laufkötter, T Miyao, J Bajorath ACS omega 4 (12), 15304-15311, 2019 | 8 | 2019 |
