研究成果

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

C. Li*, 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, 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.

H. Tang*, C. Li*, S. Jiang, H. Yu, S. Kamei, Y. Yamanishi, and Y. Morimoto, "EarlGAN: An enhanced actor-critic reinforcement learning agent-driven GAN for de novo drug design," Pattern Recognition Letters, Vol.175, pp.45-51, 2023.

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.

Z. Jiang, Z. Wang, J. Zhang, M. Wu*, C. Li*, and Y. Yamanishi, "Mode collapse alleviation of reinforcement learning-based GANs in drug design," IEEE BIBM, 2023.

W. Ye, C. Li, Y. Xie, W. Zhang, H. Zhang, B. Wang, D. Cheng*, and Z. Feng*, "Causal intervention for measuring confidence in drug-target interaction prediction," IEEE BIBM, 2023.

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.