Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore.
J Mol Graph Model. 2012 Feb;32:49-66. doi: 10.1016/j.jmgm.2011.09.002. Epub 2011 Oct 5.
Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. In this study, we developed and tested an in silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2-29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006-0.056%, 0.042-0.21%, 0.2-4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents.
选择性多靶 5-羟色胺再摄取抑制剂增强抗抑郁疗效。可以采用多种方法，包括计算机方法来促进其发现。在这项研究中，我们开发并测试了一种计算机方法，组合支持向量机（COMBI-SVM），用于从大型化合物库中对七种靶对（5-羟色胺转运体与去甲肾上腺素转运体、H（3）受体、5-HT（1A）受体、5-HT（1B）受体、5-HT（2C）受体、黑素皮质素 4 受体和神经激肽 1 受体）的多靶 5-羟色胺再摄取抑制剂进行虚拟筛选（VS）。用 917-1951 种单靶抑制剂训练的 COMBI-SVM 正确识别了从文献中收集的 6-216 种独立测试集中的 22-83.3%（多数>31.1%）的双靶抑制剂。COMBI-SVM 在错误分类为同一靶对的单靶抑制剂的 2.2-29.8%（多数<15.4%）和靶对以外的其他 6 个靶标中的 0.58-7.1%的抑制剂方面表现出中等至良好的靶标选择性。COMBI-SVM 在筛选 1700 万 PubChem 化合物、168000 MDDR 化合物和 7-8181 MDDR 化合物相似的双靶抑制剂时，显示出低的双靶抑制剂假阳性率（0.006-0.056%、0.042-0.21%、0.2-4%）。与相似性搜索、k-NN 和 PNN 方法相比，COMBI-SVM 在筛选 168000 MDDR 化合物时，产生了可比的双靶抑制剂产量、相似的靶标选择性和更低的假阳性率。COMBI-SVM 鉴定的许多 MDDR 虚拟命中的注释类与它们预测靶标的报道效果相关。COMBI-SVM 可能有助于在没有这些试剂明确知识的情况下搜索选择性多靶试剂。
