Sun’s series work in drug discovery¶
Jianfeng Sun spearheaded a research plan dedicated to the AI-based discovery of small molecule therapeutics targeting non-coding RNAs and proteins, working in collaboration with both experimentalists and computational scientists across the globe. He has released 5 studies as in Table 1. The development of DeepdlncUD was one of the series studies of this project.
Table 1:Sun’s work in drug discovery. ➵ stands for the current work.
| Field | Molecule | Tool name | Function | Technology | Publication |
|---|---|---|---|---|---|
| Systems Biology | noncoding RNA | DeepsmirUD | drug discovery | Artificial intelligence | Sun et al., 2023. International Journal of Molecular Sciences |
| DeepdlncUD | drug discovery | Artificial intelligence | Sun et al., 2023. Computers in Biology and Medicine | ||
| protein | ➵Drutai | drug discovery | Artificial intelligence | Sun et al., 2023. European Journal of Medicinal Chemistry | |
| Structural Biology | protein | DeepHelicon | structural prediction | Artificial intelligence | Sun and Frishman, 2020. Journal of Structural Biology |
| DeepTMInter | protein-protein interaction prediction | Artificial intelligence | Sun and Frishman, 2021. Computational and Structural Biotechnology Journal |
Support for running multiple cases¶
In this updated version 0.0.1 in PyPI, we tuned Drutai to make it available to run multiple drug-target interactions (DTIs). This should be seen as a major update because it is very important for researchers to screen large-scale DTIs with reduced oprations from their back ends.
Runtime¶
Drutai was developed based on molecular sequences alone. It runs in a very fast speed than those supported with complex data structures and settings, making it an ideal tool for biochemical researchers.
- Sun, J., Ru, J., Ramos-Mucci, L., Qi, F., Chen, Z., Chen, S., Cribbs, A. P., Deng, L., & Wang, X. (2023). DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. International Journal of Molecular Sciences, 24(3). 10.3390/ijms24031878
- Sun, J., Si, S., Ru, J., & Wang, X. (2023). DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning. Computers in Biology and Medicine, 163, 107226. https://doi.org/10.1016/j.compbiomed.2023.107226
- Sun, J., Xu, M., Ru, J., James-Bott, A., Xiong, D., Wang, X., & Cribbs, A. P. (2023). Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. European Journal of Medicinal Chemistry, 115500. https://doi.org/10.1016/j.ejmech.2023.115500
- Sun, J., & Frishman, D. (2020). DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks. Journal of Structural Biology, 212(1), 107574. 10.1016/j.jsb.2020.107574
- Sun, J., & Frishman, D. (2021). Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning. Computational and Structural Biotechnology Journal, 19, 1512–1530. 10.1016/j.csbj.2021.03.005