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Takuto Koyama

Showing results (1-10 of 8) with videos related to

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Journal of Chemical Information and Modeling|July 17, 2023
Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative SamplesTakuto Koyama, Shigeyuki Matsumoto, Hiroaki Iwata, et al.
Journal of Chemical Information and Modeling|November 22, 2023
VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree SearchHiroaki Iwata, Taichi Nakai, Takuto Koyama, et al.
International Journal of Pharmaceutics|February 9, 2024
Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networksHiroaki Iwata, Yoshihiro Hayashi, Takuto Koyama, et al.
Journal of Cheminformatics|January 31, 2026
Chemical genomics language model toward reliable and explainable compound-protein interaction explorationTakuto Koyama, Hayato Tsumura, Ryunosuke Okita, et al.
Journal of Cheminformatics|February 26, 2025
kMoL: an open-source machine and federated learning library for drug discoveryRomeo Cozac, Haris Hasic, Jun Jin Choong, et al.
Journal of Chemical Information and Modeling|October 22, 2025
Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead OptimizationShoma Ito, Takuto Koyama, Shigeyuki Matsumoto, et al.
Journal of Cheminformatics|January 10, 2026
Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domainsTakuto Koyama, Hiroaki Iwata, Ryosuke Kojima, et al.
Cell Death & Disease|November 11, 2024
Synergistic involvement of the NZF domains of the LUBAC accessory subunits HOIL-1L and SHARPIN in the regulation of LUBAC functionYusuke Toda, Hiroaki Fujita, Koshiki Mino, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Journal of Chemical Information and Modeling|July 17, 2023
Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative SamplesTakuto Koyama, Shigeyuki Matsumoto, Hiroaki Iwata, et al.
Journal of Chemical Information and Modeling|November 22, 2023
VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree SearchHiroaki Iwata, Taichi Nakai, Takuto Koyama, et al.
International Journal of Pharmaceutics|February 9, 2024
Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networksHiroaki Iwata, Yoshihiro Hayashi, Takuto Koyama, et al.
Journal of Cheminformatics|January 31, 2026
Chemical genomics language model toward reliable and explainable compound-protein interaction explorationTakuto Koyama, Hayato Tsumura, Ryunosuke Okita, et al.
Journal of Cheminformatics|February 26, 2025
kMoL: an open-source machine and federated learning library for drug discoveryRomeo Cozac, Haris Hasic, Jun Jin Choong, et al.
Journal of Chemical Information and Modeling|October 22, 2025
Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead OptimizationShoma Ito, Takuto Koyama, Shigeyuki Matsumoto, et al.
Journal of Cheminformatics|January 10, 2026
Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domainsTakuto Koyama, Hiroaki Iwata, Ryosuke Kojima, et al.
Cell Death & Disease|November 11, 2024
Synergistic involvement of the NZF domains of the LUBAC accessory subunits HOIL-1L and SHARPIN in the regulation of LUBAC functionYusuke Toda, Hiroaki Fujita, Koshiki Mino, et al.
Pageof 1