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通过部署先进的机器学习模型和化学信息学工具,在抗结核药物发现中导航生物活性空间:基于分子建模的回顾性研究

Ratul Bhowmik¹, Ravi Kant², Ajay Manaithiya¹

¹Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India.

Frontiers in pharmacology

|September 14, 2023

概括

计算方法加速了结核病药物发现,克服了耐药性Mycobacterium结核病菌株带来的挑战. 本综述强调了开发新型结核病治疗的关键技术,数据库和未来方向.

科学领域:

计算化学和药物发现
微生物学和传染病.
药理学和药物化学药理学和药物化学

背景情况:

结核病 (TB) 治疗受到多耐药和广泛耐药的Mycobacterium结核病菌株的阻碍.
传统药物开发是一个漫长的,几十年的过程,从目标识别到市场批准.
新型治疗策略对于对抗耐药结核病感染至关重要.

研究的目的:

审查结核病药物发现中使用的计算技术.
讨论QSAR中使用的重要性,方法,数据库和软件,药模拟和分子对接.
为突出目标,发现的分子和结核病临床试验中的药物.

主要方法:

定量结构-活动关系 (QSAR) 建模.
分子对接模拟分子对接模拟
制药学模型的建模.
数据库挖掘和分析现有的文献和临床试验数据.

主要成果:

计算方法极大地有助于识别结核病的潜在候选药物和点.
各种数据库,软件和方法方便QSAR,药模拟和分子对接.
通过这些计算策略发现了几种有前途的分子,其中一些正在进行临床试验.

结论:

计算技术对于有效的结核病药物发现至关重要,特别是针对耐药菌株.
持续推进和应用这些方法对于应对结核病药物开发方面的挑战至关重要.
未来的前景包括完善现有技术,探索新型结核病治疗的新计算策略.

关键词:

在QSAR中使用QSAR.药物耐药性耐药性药物耐药性分子对接的分子对接.制药器模型的制药模型.结核病是一种肺结核病.

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