Introduction: The nuclear transcription factor PPARγ, which can modulate cell growth via proliferation and apoptosis-related mechanisms, is a promising target in cancer therapy. This study aims to focus on PPARγ as the target and use virtual screening to find hits. Methods: A set of 5,677 flavonoid compounds were filtered by subjecting them to descriptor-based drug-likeness and ADMET strategies to discover drug-like compounds. The candidates' modes of binding to PPARγ were then evaluated using docking and MD simulation. PharmMapper was used to identify the potential targets of selected hits. The pharmacological network was constructed based on the GO and KEGG pathway analysis. Results: In primary screening, 3,057 compounds met various drug-likeness criteria and docked well as partial agonists in the PPARγ-LBD. Five compounds (euchrenone b1, kaempferol-7-Orhamnoside, vincetoxicoside B, morusin, and karanjin) were selected with the use of ADMET profiles for further MD simulation investigation. Based on the PharmMapper findings, 52 proteins were then submitted to GO and KEGG enrichment analysis. As expected by GO and KEGG pathway enrichment studies, core targets were enriched in the PI3K-Akt signaling pathway (p < 0.01), indicating that certain chemicals may be involved in cancer processes. Conclusion: Our results suggested that the selected compounds might have sufficient drug-likeness, pharmacokinetics, and in silico bioactivity by acting as PPARγ partial agonists. Although much work remains to illuminate extensive cancer therapeutic/ chemopreventive efficacy of flavonoids in vivo, in silico methodology of our cheminformatics research may be able to provide additional data regarding the efficacy and safety of potential candidates for therapeutic targets.

中文翻译：

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基于天然的 PPARγ 受体部分激动剂的比较计算筛选

简介：核转录因子 PPARγ 可通过增殖和凋亡相关机制调节细胞生长，是癌症治疗中一个有前途的靶点。本研究旨在以 PPARγ 为目标，并使用虚拟筛选来寻找命中。方法：通过对一组 5,677 种类黄酮化合物进行基于描述符的药物相似性和 ADMET 策略筛选，以发现药物样化合物。然后使用对接和 MD 模拟评估候选者与 PPARγ 的结合模式。PharmMapper 用于识别选定命中的潜在目标。基于GO和KEGG通路分析构建药理学网络。结果：在初步筛选中，3,057 种化合物符合各种药物相似性标准，并作为 PPARγ-LBD 的部分激动剂对接良好。五种化合物（euchrenone b1，kaempferol-7-Orhamnoside、vincetoxicoside B、morusin 和 karanjin) 使用 ADMET 配置文件进行选择，用于进一步的 MD 模拟研究。基于 PharmMapper 的发现，随后将 52 种蛋白质提交给 GO 和 KEGG 富集分析。正如 GO 和 KEGG 通路富集研究所预期的那样，核心靶标在 PI3K-Akt 信号通路中富集 (p < 0.01)，表明某些化学物质可能参与癌症过程。结论：我们的结果表明，通过充当 PPARγ 部分激动剂，所选化合物可能具有足够的药物相似性、药代动力学和计算机生物活性。尽管还有很多工作要阐明类黄酮在体内广泛的癌症治疗/化学预防功效，