TAKUTO KOYAMA
PORTFOLIO
Research on AI drug discovery utilizing chemoinformatics,
with a particular focus on in silico screening.
ABOUT
I'm a PhD student in Human Health Science at Kyoto University, specializing in chemo/bioinformatics for drug discovery, supervised by Prof. Yasushi Okuno.
My research focuses on developing and applying advanced deep learning models, including language models and graph neural networks, to accurately predict drug-target interactions.
My goal is to leverage these computational skills to accelerate the identification of novel therapeutic candidates.
BIOGRAPHY
2024-present: Enrolled in the Doctoral Program in Human Health Sciences, Graduate School of Medicine, Kyoto University
2024-present: JSPS Research Fellowship for Young Scientists (DC1)
2024: Completed the Master's Program in Human Health Sciences, Graduate School of Medicine, Kyoto University
2022: Graduated from the Department of Pharmaceutical Sciences, University of Tokyo
2020: Entered the Faculty of Pharmacy, University of Tokyo
2018: Admitted to the University of Tokyo (Science Category I)
RECENT WORKS
ARTICLES
Chemical Genomics Language Model toward Reliable and Explainable Compound-Protein Interaction Exploration
Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples
Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead Optimization
kMoL: an open-source machine and federated learning library for drug discovery
Synergistic involvement of the NZF domains of the LUBAC accessory subunits HOIL-1L and SHARPIN in the regulation of LUBAC function
Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks
VGAE-MCTS: a New Molecular Generative Model combining Variational Graph Auto-Encoder and Monte Carlo Tree Search
PRESENTATIONS
Takuto Koyama, Hayato Tsumura, Ryunosuke Okita, Kimihiro Yamazaki, Keiko Imamura, Takashi Kato, Aki Hasegawa, Hiroaki Iwata, Ryosuke Kojima, Haruhisa Inoue, Shigeyuki Matsumoto, and Yasushi Okuno. Chemical Genomics Language Model toward Reliable and Explainable Compound-Protein Interaction Exploration, The 7th Annual Meeting of Japanese Association for Medical Artificial Intelligence, Kyoto, June, 2025. [ORAL]
Takuto Koyama, Hayato Tsumura, Ryunosuke Okita, Kimihiro Yamazaki, Takashi Kato, Aki Hasegawa, Hiroaki Iwata, Ryosuke Kojima, Shigeyuki Matsumoto, and Yasushi Okuno.
“Large-scale Prediction of Compound-Protein Interactions Using a Chemical-Genomics Language Model”,
47th Chemoinformatics Symposium, Kanazawa, December 2024. [ORAL]
Takuto Koyama, Hayato Tsumura, Ryunosuke Okita, Ryosuke Kojima, Shigeyuki Matsumoto, Yasushi Okuno.
ChemGLaM: Chemical Genomics Language Models for Compound-Protein Interaction Prediction,
ISMB 2024 MLCSB, Montreal, Canada, July 2024. [POSTER]
Takuto Koyama, Hayato Tsumura, Atsuyuki Matsumoto, Ryunosuke Okita, Ryosuke Kojima, Yasushi Okuno.
“ChemGLaM: Compound-Protein Interaction Prediction Using Large Language Models”,
24th Annual Meeting of the Protein Society, Sapporo, June 2024. [POSTER] Awarded Poster Prize
Takuto Koyama, Hiroaki Iwata, Atsuyuki Matsumoto, Ryosuke Kojima, Takao Otsuka, Aki Hasegawa, and Yasushi Okuno.
“Insight into Federated Learning for Compound-Protein Interaction Prediction”,
CBI Society 2023 Annual Meeting, Tokyo, October 2023. [ORAL]
Akira Ito, Takuto Koyama, Hiroaki Iwata, Atsuyuki Matsumoto, and Yasushi Okuno.
“Exploring Chemical Structural Insights of ADME Properties via Interpretable Deep Learning”,
CBI Society 2023 Annual Meeting, Tokyo, October 2023. [POSTER]
Hiroaki Iwata, Daichi Nakai, Takuto Koyama, Atsuyuki Matsumoto, Ryosuke Kojima, and Yasushi Okuno.
“A New Molecular Generation Method Combining Deep Learning and Reinforcement Learning”,
CBI Society 2023 Annual Meeting, Tokyo, October 2023. [POSTER]
Takuto Koyama, Shigeyuki Matsumoto, Hiroaki Iwata, Ryosuke Kojima, Yasushi Okuno.
Iterative Data Augmentation of Near-Boundary Negative Samples Improves the Model Generalizability in Compound-Protein Interaction Prediction,
ISMB/ECCB 2023 MLCSB, Lyon, France, July 2023. [POSTER]
Hiroaki Iwata, Yoshihiro Hayashi, Takuto Koyama, Aki Hasegawa, Satoshi Terayama, and Yasushi Okuno.
“Clustering of Pharmaceutical Excipients Using Pretrained Convolutional Neural Networks”,
38th Annual Meeting of the Japanese Pharmaceutical Association, Aichi, May 2023. [POSTER]
Takuto Koyama, Atsuyuki Matsumoto, Hiroaki Iwata, Ryosuke Kojima, and Yasushi Okuno.
“Improvement of Compound-Protein Interaction Prediction with Semi-supervised Learning”,
CBI Society 2022 Annual Meeting, Tokyo, October 2022. [POSTER]
WORK EXPERIENCES
- Part-time Engineer, Preferred Networks, Inc.
- August 1, 2025 – Present
- Invited Researcher, Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd.
- January 20, 2025 – April 21, 2025
AWARDS
Oral Presentation Award at the 7th The 7th Annual Meeting of Japanese Association for Medical Artificial Intelligence
Poster Award Winner at the 24th Annual Meeting of the Protein Society
WRITINGS
Authored the AI Drug Discovery Guide for the 'Demon Slayer: Kimetsu no Yaiba' Collaboration
Takuto Koyama, Yasushi Okuno,
“Chapter 6: AI, Big Data, and Drug Discovery” in Utilization of Information and AI in Chemistry (CSJ Current Review: 50), Kagaku Dojin
Takuto Koyama, Yasushi Okuno,
The AI Trends in Chemical Space for Drug Discovery, Drug Development Supported by Informatics, Springer
REVIEW
SCHOLARSHIPS
SKILL
Python
Experience in research activities using Python coding
and implementation of deep learning models with PyTorch and TensorFlow.
English
TOEIC score: 905
Statistics
Level 1 Certification in Statistics
Mathematics
JDLA E Certification
5th Dan in Abacus Calculation
7th Dan in Mental Arithmetic