研究成果

C. Li*, H. Tang, Y. Zhu, and Y. Yamanishi, “A reinforcement learning -driven Transformer GAN for molecular generation,” Machine Intelligence Research, 2025. 

S. Namba, N. Otani, C. Li, and Y. Yamanishi, “SSL-VQ: Vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases,” Bioinformatics, 2025.

C. Li* and Y. Yamanishi, “AI-driven transcriptome profile-guided hit molecule generation,” Artificial Intelligence, 2024. 

K. Yasuda, F. Berenger, K. Amaike, A. Ueda, T. Nakagomi, G. Hamasaki, C. Li, N. Y. Otani, K. Kaitoh, K. Tsuda, K. Itami, and Y. Yamanishi*, “De novo generation of dual-target compounds using artificial intelligence,” iScience, 2024.

Y. Matsukiyo, A. Tengeiji, C. Li, and Y. Yamanishi*, "Transcriptionally conditional recurrent neural network for de novo drug design," Journal of Chemical Information and Modeling, 2024.

C. Li* and Y. Yamanishi, "Gx2Mol: De novo generation of hit-like molecules from gene expression profiles," ECML-PKDD, 2025. 

H. Tang, C. Li*, S. Kamei, Y. Yamanishi, and Y. Morimoto, "InstGAN: Instant actor-critic-driven GAN for de novo molecule generation and property optimization," IJCAI, 2025.

C. Li* and Y. Yamanishi, "TenGAN: Pure transformer encoders make an efficient discrete GAN for de novo molecular generation," AISTATS, 2024.

C. Li* and Y. Yamanishi, "GxVAEs: Two joint VAEs generate hit molecules from gene expression profiles," AAAI, 2024.

C. Li* and Y. Yamanishi, "SpotGAN: A reverse-transformer GAN generates scaffold-constrained molecules with property optimization," ECML-PKDD, 2023. 

C. Li*, K. Kaitoh, C. Yamanaka, and Y. Yamanishi, "Transformer-based objective-reinforced generative adversarial network to generate desired molecules," IJCAI, 2022.