STAT3 属于七个转录因子家族的一员。它在激活参与多种细胞过程的各种基因转录中起重要作用。STAT3 的高水平在数种癌症中得到检测到。因此，STAT3 抑制被认为是一种有前景的抗癌治疗策略。然而，由于STAT3 抑制剂结合于该蛋白的浅层 SH2 域，预计水合水分子在配体结合中起到了重要作用，使得有效配体的发现变得复杂。为了解决这个问题，我们在此提出从 STAT3 SH2 域中一种有效的共结晶配体的分子动力学 (MD) 帧中提取药效团的方法。随后，我们采用基因功能算法联合机器学习 (GFA-ML) 来探索在一系列抑制剂中能解释生物活性变化的 MD-导出药效团的最佳组合。为增加数据集的规模，我们考虑了配体的多重构象，将训练和测试列表扩增近百倍。经过188 纳秒的 MD 模拟后，出现了一种显著的药效团，代表 STAT3-配体结合。使用该模型对国家癌症研究所（NCI）数据库进行筛选，发现了一种与 STAT3 的 SH2 域结合并抑制该途径的反应物，其抑制浓度在低微摩尔级别。©2023. 作者，由 Springer Nature Switzerland AG 独家授权。

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.