To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein–ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of TβR1, with a successful docking rate of 95%. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of TβR1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.

中文翻译：

---

基于对接的 TβR1 抑制剂虚拟筛选：姿势预测和评分功能的评估。

为了提高转化生长因子-β 1 型受体（TβR1）抑制剂虚拟筛选的可靠性，本研究对 Discovery Studio 软件中的 2 种对接方法和 11 种评分功能进行了评估和验证。LibDock 和 CDOCKER 方案在 24 个 TβR1 蛋白-配体复合物的测试集上进行。基于对接姿势和共晶构象之间的均方根偏差（RMSD）值（以Å为单位），可以有效地应用CDOCKER协议在TβR1的中等规模虚拟筛选实验中获得更准确的对接，对接成功率达95%。使用包含 281 个已知活性配体和 8677 个非活性配体的数据集来确定最佳评分函数。接受者操作特征（ROC）曲线用于比较评分函数在将最佳分数归因于活性配体和非活性配体方面的性能。结果表明，Ludi 1、PMF、Ludi 2、Ludi 3、PMF04、PLP1、PLP2、LigScore2、Jain和LigScore1是比随机分布模型更好的评分函数，AUC分别为0.864、0.856、0.842、0.812、0.776、0.774分别为 0.769、0.762、0.697 和 0.660。基于ROC曲线的两两比较，选择Ludi 1和PMF作为TβR1抑制剂虚拟筛选的最佳评分函数。进一步的富集因子 (EF) 分析也支持 PMF 和 Ludi 1 作为排名前两位的评分函数。