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基于深度学习的预测,在致病性菌根菌中化学积累

Mark R Sullivan¹, Eric J Rubin¹

¹Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.

bioRxiv : the preprint server for biology

|January 7, 2025

概括

设计有效的抗生素需要了解药物如何进入细菌细胞. 这项研究使用深度学习来预测Mycobacterium的药物积累,改善抗生素的开发.

科学领域:

微生物学微生物学
药理学药理学是指药理学的学科.
计算生物学计算生物学

背景情况:

药物在目标部位的积累对于治疗疗效至关重要.
细菌细胞壁对药物吸收存在重大障碍,阻碍了抗生素的开发.
预测哪些药物结构促进或阻碍细菌吸收是具有挑战性的.

研究的目的:

为了研究 * Mycobacterium abscessus * 中的药物积累.
开发细菌药物吸收的预测模型.
根据积累和疗效,识别潜在的抗生素候选者.

主要方法:

测量了1528种已批准的药物的积累 * Mycobacterium abscessus * 使用液体染色体质谱学.
训练深度学习模型来预测药物积累.
验证了模型准确性与最初库中没有的多种化合物.

主要成果:

简单的化学特性不能有效地预测菌根菌中的药物积累.
深度学习模型准确地预测了M.的药物积累.
药物吸收水平与正在开发中的药物的疗效相关.

结论:

不同的药物吸收是抗生素对M.的有效性的一个关键因素.
预测算法可以指导选择有前途的抗生素候选者.
计算方法可以增强传统的药物发现方法,以挑战病原体.

关键词:

吸收药物吸收的情况化学透性化学透性深度学习是一种深度学习.菌根细菌 (mycobacteria) 是一种细菌.

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