Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models. - Matthew Ragoza; Tomohide Masuda; David Ryan Koes (CoRR, 2020)

Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models. - Tomohide Masuda; Matthew Ragoza; David Ryan Koes (CoRR, 2020)

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. - Paul G. Francoeur; Tomohide Masuda; Jocelyn Sunseri; Andrew Jia; Richard B. Iovanisci; Ian Snyder; David Ryan Koes (J. Chem. Inf. Model., 2020)

High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning. - Koichiro Kato; Tomohide Masuda; Chiduru Watanabe; Naoki Miyagawa; Hideo Mizouchi; Shumpei Nagase; Kikuko Kamisaka; Kanji Oshima; Satoshi Ono; Hiroshi Ueda; Atsushi Tokuhisa; Ryo Kanada; et al. (J. Chem. Inf. Model., 2020)

Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models. - Matthew Ragoza; Tomohide Masuda; David Ryan Koes (CoRR, 2021)

GNINA 1.0: molecular docking with deep learning. - Andrew T. McNutt; Paul G. Francoeur; Rishal Aggarwal; Tomohide Masuda; Rocco Meli; Matthew Ragoza; Jocelyn Sunseri; David Ryan Koes (J. Cheminformatics, 2021)

Quantitative Structure-Activity Relationships for Human Galectin-3 Inhibitors: Insights from Quantum Chemical Interaction Energy Terms. - Tomohide Masuda; Chiduru Watanabe; Koichiro Kato; Teruki Honma; Masateru Ohta; Mitsunori Ikeguchi (J. Chem. Inf. Model., 2025)