Polypharmacological Drug Design Guided by Integrating Phenotypic and Restricted Fragment Docking Strategies
Polypharmacological drugs are of great value for treating complex human diseases by the combinative modulation of several biological targets. However, multitarget drug design with more than two targets is challenging and generally discovered by serendipity. Herein, a restricted fragment docking (RFD) computational method combined with a phenotypic discovery approach was developed for rational polypharmacological drug design. Via genetic and drug combination studies in a microglial phenotype, we first identified novel synergistic effects by triple target modulation toward RIPK1, MAP4K4, and ALK. Drawing on the RFD method to explore virtual chemical spaces in three target pockets, we identified a lead compound, <b>LP-10d</b>, that precisely modulated RIPK1/MAP4K4/ALK for synergistic microglial protection with low nanomolar potency. <b>LP-10d</b> showed polypharmacology against multiple neuropathologies in the 3xTg Alzheimer's disease mouse model. Our study revealed a potential application of the RFD method, which is valuable to further polypharmacological drug discovery involved in clinical studies for treating complex human diseases.
Authors
- Fucheng Yin (ORCID: https://orcid.org/0000-0002-9999-6367)
- Yifan Chen (ORCID: https://orcid.org/0000-0002-9677-9340)
- Lingyi Kong (ORCID: https://orcid.org/0000-0001-9712-2618)
- Yonglei Zhang (ORCID: https://orcid.org/0000-0002-1452-0109)
- Liangliang Ma
- Xinxin Li (ORCID: https://orcid.org/0000-0001-5624-5689)
- Xiaobing Wang (ORCID: https://orcid.org/0000-0001-5044-3721)
- Shang Li (ORCID: https://orcid.org/0000-0001-8027-5167)
- Zhongwen Luo
- Siyuan Wan (ORCID: https://orcid.org/0009-0002-1965-7056)
Institutions
- China Pharmaceutical University (CN)
- State Key Laboratory of Natural Medicine
Publication Details
- Journal
- Journal of Medicinal Chemistry
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1021/acs.jmedchem.4c01731
- Citations
- 4
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.45
Funders
- Ministry of Education of the People's Republic of China
- State Administration of Foreign Experts Affairs
- Natural Science Foundation of Jiangsu Province
- Six Talent Peaks Project in Jiangsu Province
- “333 Project” of Jiangsu Province
- Double First Class University Plan