1.Muttenthaler M , King G F , Adams D J , et al. Trends in peptide drug discovery[J]. Nature Reviews Drug Discovery
, 2021, 20(4).2. Rastogi, S., Shukla, S., Kalaivani, M. and Singh, G.N., 2019. Peptide-based therapeutics: quality specifications, regulatory considerations, and prospects. Drug discovery today, 24(1), pp.148-162.3. Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., ... & Zeng, J. (2017). A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature communications, 8(1), 1-13.4. Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019). NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics, 35(1), 104-111.5. Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L., ... & Zeng, J. (2019). DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening. Genomics, Proteomics & Bioinformatics, 17(5), 478-495.6. Li, S., Wan, F., Shu, H., Jiang, T., Zhao, D. and Zeng, J., 2020. MONN: a multi-objective neural network for predicting compound-protein interactions and affinities. Cell Systems, 10(4), pp.308-322.7. Hong, L., Lin, J., Li, S., Wan, F., Yang, H., Jiang, T., Zhao, D. and Zeng, J., 2020. A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nature Machine Intelligence, 2(6), pp.347-355.
