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.

STAT3 属于七个转录因子家族。它在激活参与多种细胞过程的多种基因的转录中发挥着重要作用。在多种类型的癌症中均检测到高水平的 STAT3。因此，STAT3抑制被认为是一种有前途的抗癌治疗策略。然而，由于 STAT3 抑制剂与蛋白质的浅层 SH2 结构域结合，因此预计水合水分子在配体结合中发挥重要作用，从而使有效结合剂的发现变得复杂。为了解决这个问题，我们在此建议从 STAT3 SH2 结构域内复合的有效共结晶配体的分子动力学 (MD) 框架中提取药效团。随后，我们采用遗传函数算法与机器学习 (GFA-ML) 相结合来探索 MD 衍生药效团的最佳组合，该组合可以解释一系列抑制剂之间生物活性的变化。为了增强数据集，通过考虑配体的多个构象异构体，训练和测试列表增加了近 100 倍。MD 模拟 188 ns 后出现一个显着的药效团，代表 STAT3-配体结合。使用该模型筛选美国国家癌症研究所 (NCI) 数据库，发现一种低微摩尔抑制剂最有可能与 STAT3 的 SH2 结构域结合并抑制该通路。