De novo design of mIDH1 inhibitors by integrating deep learning and molecular modeling
In present study, compounds M1, M2, M3 and M6 generated by BRNN exhibited optimal binding properties. This study is the first attempt to use deep learning to design mIDH1 inhibitors, which provides theoretical guidance for the design of mIDH1 inhibitors.
Authors
- Minh Phương Tống
- Weitong Zhang (ORCID: https://orcid.org/0000-0001-6254-161X)
- Xueying Liu (ORCID: https://orcid.org/0000-0003-4395-0228)
- Yuwei Wang (ORCID: https://orcid.org/0000-0001-5782-1107)
- Wei Zhao (ORCID: https://orcid.org/0000-0001-9437-3198)
- Jialong Liang
- Lulu Xu (ORCID: https://orcid.org/0000-0002-7333-3717)
- Dingkang Sun
Institutions
- National Clinical Research Center for Digestive Diseases (CN)
- Karamay Central Hospital of Xinjiang (CN)
- Xijing Hospital (CN)
- Shaanxi University of Chinese Medicine (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2024-10-23
- DOI
- https://doi.org/10.3389/fphar.2024.1491699
- Citations
- 5
- Primary Topic
- Protein Degradation and Inhibitors
- Type
- article
- Field-Weighted Citation Impact
- 1.17
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shaanxi Province
- Shaanxi University of Chinese Medicine