Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a Benchmark Data Set
Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a Benchmark Data Set
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在基准数据集上评估基于规则的虚拟筛选技术 (INDDEx)
DOI：
10.1021/jp212084f
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Reynolds C
中科院分区：
文献类型：
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作者：
Reynolds C
研究性新药发现实例 (INDDEx) 包的开发目的是通过将活性与化学子结构联系起来来寻找活性化合物，并指导进一步药物开发的过程。 INDDEx是一种机器学习技术，基于形成有关活性分子的子结构特征的定性逻辑规则，对规则进行加权以形成定量模型，然后使用该模型筛选分子数据库。 INDDEx 被证明能够从多种活性化合物中学习，并且在进行虚拟筛选时可用于支架跳跃，即使在从少量化合物中学习时也能提供高检索率。在测试的数据集中，在 1% 的数据中，当从 2、4 和 8 个活性配体学习时，INDDEx 的平均富集因子分别为 69.2、82.7 和 90.4。在 0.1% 的数据中，当从 2、4 和 8 个活性配体学习时，INDDEx 的平均富集因子分别为 492、631 和 707。排除所有具有超过 0.5 Tanimoto 最大公共子结构的配体，当从 2、4 和 8 个活性配体学习时，INDDEx 的平均富集因子分别为 1%，分别为 52.3、63.6 和 66.9。 INDDEx 的性能与 eHiTS LASSO、PharmaGist 和 DOCK 进行了比较。
The Investigational Novel Drug Discovery by Example (INDDEx) package has been developed to find active compounds by linking activity to chemical substructure and to guide the process of further drug development. INDDEx is a machine-learning technique, based on forming qualitative logical rules about substructural features of active molecules, weighting the rules to form a quantitative model, and then using the model to screen a molecular database. INDDEx is shown to be able to learn from multiple active compounds and to be useful for scaffold-hopping when performing virtual screening, giving high retrieval rates even when learning from a small number of compounds. Across the data sets tested, at 1% of the data, INDDEx was found to have average enrichment factors of 69.2, 82.7, and 90.4 when learning from 2, 4, and 8 active ligands, respectively. At 0.1% of the data, INDDEx had average enrichment factors of 492, 631, and 707 when learning from 2, 4, and 8 active ligands, respectively. Excluding all ligands with more than 0.5 Tanimoto Maximum Common Substructure, INDDEx had average enrichment factors at 1% of 52.3, 63.6, and 66.9 when learning from 2, 4, and 8 active ligands, respectively. The performance of INDDEx is compared with that of eHiTS LASSO, PharmaGist, and DOCK.