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发现NDM-1抑制剂使用分子亚结构嵌入表征.

Thomas Papastergiou1,2, Jérôme Azé1, Sandra Bringay1,3

  • 1LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France.

Journal of integrative bioinformatics
|July 27, 2023
PubMed
概括

开发了一种新的数据库和机器学习方法,以对抗新德里金属β-乳糖酶-1 (NDM-1) 酶介导的抗生素耐药性. 与传统技术相比,这种方法显著提高了分类准确性.

关键词:
作为NDM-1抑制剂的使用.发现药物的发现.机器学习是机器学习.多个实例的学习学习多个实例的学习.

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科学领域:

  • 生物化学和分子生物学
  • 计算生物学和化学信息学
  • 传染病与微生物学

背景情况:

  • 新德里金属β-乳糖酶-1 (NDM-1) 是一种关键的酶,可以使细菌对广泛的抗生素产生抗药性.
  • 管理和预测NDM-1活动对于开发新的抗微生物战略至关重要.
  • 分析抗生素耐药机制的现有方法需要提高准确性和范围.

研究的目的:

  • 建立一个精心策划的NDM-1生物活性数据库.
  • 开发和验证一种用于分类NDM-1活动的新计算框架.
  • 通过数据库扫描,识别针对NDM-1的强效化合物.

主要方法:

  • 创建一个统一的NDM-1生物活性数据库,标准化规则.
  • 多个实例学习 (MIL) 的应用与分子子结构嵌入.
  • 使用k-fold交叉验证和超参数优化开发一个整体排名和分类框架.
  • 研究紧的分子表征 (原子和二原子的亚结构).
  • 对药物银行数据库的高活性化合物的选.

主要成果:

  • 开发的MIL范式在平衡精度方面比传统的机器学习方法显著提高了45.7%.
  • 该框架在分类NDM-1活动方面显示出有希望的概括能力.
  • 该研究确定并排名了来自药品银行的NDM-1最活跃的前15种化合物.

结论:

  • 新的基于MIL的计算框架为分析NDM-1生物活性提供了强大而准确的方法.
  • 精心策划的数据库和确定的规则为NDM-1研究提供了宝贵的资源.
  • 已识别的强效化合物代表了对NDM-1-介导抗生素耐药性的未来药物开发的有希望的候选者.