RET酪氨酸激酶的激活在各种癌症的发病机制中起着至关重要的作用。包括非小细胞肺癌,甲状腺乳头状癌,多发性内分泌肿瘤2A和2B型(MEN2A,MEN2B),家族性甲状腺髓样癌.RET原癌基因中的基因融合和点突变导致RET信号通路的组成型激活。因此,开发针对RET的有效抑制剂至关重要。小分子通过与RET的激酶结构域结合并阻断其酶活性而显示出作为抑制剂的前景。然而,由于单个氨基酸变化而产生的抗性的出现提出了重大挑战。在这项研究中,提出了一种基于结构的动态药效团驱动的方法,该方法使用分子动力学轨迹的E药效团建模来选择低能有利的假设,和ML训练的QSAR模型来预测化合物的pIC50值。为了这个目标,使用开发的基于配体的模型筛选了广泛的小分子文库,并提出了能够抑制RET激活的有效化合物。
Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.