Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data. - Hiroaki Iwata; Tatsuru Matsuo; Hideaki Mamada; Takahisa Motomura; Mayumi Matsushita; Takeshi Fujiwara; Kazuya Maeda; Koichi Handa (J. Chem. Inf. Model., 2022)

On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data. - Koichi Handa; Morgan C. Thomas; Michiharu Kageyama; Takeshi Iijima; Andreas Bender 002 (J. Cheminformatics, 2023)

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models. - Koichi Handa; Shunta Sasaki; Satoshi Asano; Michiharu Kageyama; Takeshi Iijima; Andreas Bender 002 (J. Chem. Inf. Model., 2024)

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue. - Koichi Handa; Saki Yoshimura; Michiharu Kageyama; Takeshi Iijima (J. Chem. Inf. Model., 2024)