Ring system-based chemical graph generation for de novo molecular design. - Tomoyuki Miyao; Hiromasa Kaneko; Kimito Funatsu (J. Comput. Aided Mol. Des., 2016)

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x). - Tomoyuki Miyao; Hiromasa Kaneko; Kimito Funatsu (J. Chem. Inf. Model., 2016)

Exploring differential evolution for inverse QSAR analysis. - Tomoyuki Miyao; Kimito Funatsu; Jürgen Bajorath (F1000Research, 2017)

Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching. - Tomoyuki Miyao; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2018)

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships. - Tomoyuki Miyao; Swarit Jasial; Jürgen Bajorath; Kimito Funatsu (J. Comput. Aided Mol. Des., 2019)

Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties. - Tomoyuki Miyao; Kimito Funatsu (J. Chem. Inf. Model., 2019)

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling. - Tomoyuki Miyao; Kimito Funatsu; Jürgen Bajorath (J. Chem. Inf. Model., 2019)

Three-Dimensional Activity Landscape Models of Different Design and Their Application to Compound Mapping and Potency Prediction. - Tomoyuki Miyao; Kimito Funatsu; Jürgen Bajorath (J. Chem. Inf. Model., 2019)

Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure. - Shunsuke Tamura; Tomoyuki Miyao; Kimito Funatsu (J. Chem. Inf. Model., 2019)

Exploring Topological Pharmacophore Graphs for Scaffold Hopping. - Hiroshi Nakano; Tomoyuki Miyao; Kimito Funatsu (J. Chem. Inf. Model., 2020)

Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations. - Akinori Sato; Tomoyuki Miyao; Swarit Jasial; Kimito Funatsu (J. Comput. Aided Mol. Des., 2021)

Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping. - Hiroshi Nakano; Tomoyuki Miyao; Swarit Jasial; Kimito Funatsu (J. Chem. Inf. Model., 2021)

Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression. - Itsuki Maeda; Akinori Sato; Shunsuke Tamura; Tomoyuki Miyao (J. Comput. Aided Mol. Des., 2022)

NuclSeg: nuclei segmentation using semi-supervised stain deconvolution. - Haixin Wang; Jian Yang; Ryohei Katayama; Michiya Matsusaki; Tomoyuki Miyao; Jinjia Zhou (MMAsia, 2023)

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. - Shunsuke Tamura; Tomoyuki Miyao; Jürgen Bajorath (J. Cheminformatics, 2023)

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test. - Itsuki Maeda; Shunsuke Tamura; Yoshihiro Ogura; Takayuki Serizawa; Takashi Shimada; Ryo Kunimoto; Tomoyuki Miyao (J. Chem. Inf. Model., 2024)

Analog Accessibility Score (AAscore) for Rational Compound Selection. - Takato Ue; Akinori Sato; Tomoyuki Miyao (J. Chem. Inf. Model., 2024)

Understanding Conformation Importance in Data-Driven Property Prediction Models. - Yu Hamakawa; Tomoyuki Miyao (J. Chem. Inf. Model., 2025)

Robotic System for Chemical Experiment Automation with Dual Demonstration of End-effector and Jig Operations. - Hikaru Sasaki; Naoto Komeno; Takumi Hachimine; Kei Takahashi; Yu-ya Ohnishi; Tetsunori Sugawara; Araki Wakiuchi; Miho Hatanaka; Tomoyuki Miyao; Hiroharu Ajiro; Mikiya Fujii; Takamitsu Matsubara (CoRR, 2025)