• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Genomics >Molib: A machine learning based classification tool for the prediction of biofilm inhibitory molecules
【24h】

Molib: A machine learning based classification tool for the prediction of biofilm inhibitory molecules

机译：MOLIB：基于机器学习的分类工具，用于预测生物膜抑制分子

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Identification of biofilm inhibitory small molecules appears promising for therapeutic intervention against biofilm-forming bacteria. However, the experimental identification of such molecules is a time-consuming task, and thus, the computational approaches emerge as promising alternatives. We developed the `Molib' tool to predict the biofilm inhibitory activity of small molecules. We curated a training dataset of biofilm inhibitory molecules, and the structural and chemical features were used for feature selection, followed by algorithms optimization and building of machine learning-based classification models. On five-fold cross validation, Random Forest-based descriptor, fingerprint and hybrid classification models showed accuracies of 0.93, 0.88 and 0.90, respectively. The performances of all models were evaluated on two different validation datasets including biofilm inhibitory and non-inhibitory molecules, attesting to its accuracy (>= 0.90). The Molib web server would serve as a highly useful and reliable tool for the prediction of biofilm inhibitory activity of small molecules.
机译：生物膜抑制性小分子的鉴定似乎对生物膜形成细菌的治疗干预似乎有望。然而，这种分子的实验鉴定是耗时的任务，因此，计算方法作为有前途的替代方案出现。我们开发了“Molib”工具，以预测小分子的生物膜抑制活性。我们策划了生物膜抑制分子的训练数据集，并且结构和化学特征用于特征选择，其次是算法优化和基于机器学习的分类模型的构建。在五倍交叉验证，随机林的描述符，指纹和混合分类模型分别显示为0.93,0.88和0.90的精度。在包括生物膜抑制和非抑制分子的两种不同验证数据集上评估所有模型的性能，证明其精度（> = 0.90）。 MOLIB Web服务器将作为预测小分子的生物膜抑制活性的非常有用和可靠的工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号