Naïve Bayesian Models for Vero Cell Cytotoxicity.
Naïve Bayesian Models for Vero Cell Cytotoxicity.
复制标题
DOI：
10.1007/s11095-018-2439-9
复制
发表时间：
2018-06-29
影响因子：
3.7
通讯作者：
Freundlich JS
中科院分区：
文献类型：
--
作者：
Perryman AL;Patel JS;Russo R;Singleton E;Connell N;Ekins S;Freundlich JS
为了推进针对感染性微生物的潜在治疗性小分子的转化研究，化合物必须显示出相对缺乏哺乳动物细胞毒性。Vero细胞毒性（CC50）是该指标的常见初始试验。我们探讨了朴素贝叶斯模型的发展，可以提高识别非细胞毒性化合物的概率。在PubChem中鉴定Vero细胞细胞毒性测定，重新格式化并策划以创建具有8741个独特小分子的训练集。这些数据用于开发贝叶斯分类器，这些分类器通过内部交叉验证进行评估，使用我们实验室的193种化合物进行外部测试，并使用PubChem的1609种独特化合物进行独立验证。用独立的、外部的测试集和验证集进行的评价表明，用ECFP_6描述符构建的细胞毒性贝叶斯模型比用FCFP_6指纹图谱构建的模型更准确。根据常规和机会校正统计以及富集因子，最佳细胞毒性贝叶斯模型在外部评价中显示出预测能力。外部测试的结果表明，我们的新型细胞毒性贝叶斯模型显示出足够的预测能力，以帮助指导转化研究。为了帮助化学工具和药物发现社区，我们策划的培训集作为补充材料的一部分分发。
To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds. Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem. Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors. The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material.
登录
查看更多内容
影响因子：
4.1
作者：
Rodgers, Amie D.;Zhu, Hao;Fourches, Denis;Rusyn, Ivan;Tropsha, Alexander
通讯作者：
Tropsha, Alexander
影响因子：
4.1
作者：
Greene, Nigel;Fisk, Lilia;Pelletier, Dennis J.
通讯作者：
Pelletier, Dennis J.
影响因子：
3.5
作者：
Kim, Hyunsoo;Yoon, Sung Chul;Jeong, Damon
通讯作者：
Jeong, Damon
结合命中率优化的计算方法在结核分枝杆菌中的铅优化。
影响因子：
3.7
作者：
Ekins, Sean;Freundlich, Joel S.;Hobrath, Judith V.;White, E. Lucile;Reynolds, Robert C.
通讯作者：
Reynolds, Robert C.
影响因子：
3.8
作者：
Lin, Zhiwu;Will, Yvonne
通讯作者：
Will, Yvonne