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使用深度学习和理性修改设计抗微生物:在细菌,Candida albicans和癌细胞中的活性

Andrea Mesa¹, Andrés Orrego², John W Branch-Bedoya²

¹Departamento de Biociencias, Facultad de Ciencias, Grupo Biología Funcional, Universidad Nacional de Colombia, Sede Medellín, Carrera 65 #59A-110, Medellín, 050034, Colombia. anmesago@unal.edu.co.

Current microbiology

|July 11, 2025

概括

人工智能优化了抗微生物来对抗日益增长的耐药性. 这些新型体显示出高抗菌活性和潜在的抗癌特性,为传统抗生素提供了有希望的替代品.

科学领域:

生物化学生物化学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

抗菌素耐药性是一个关键的全球健康威胁,需要新的治疗策略.
抗微生物 (AMP) 是传统抗生素的一个有希望的替代品,因为它们具有独特的作用机制.
人工智能 (AI) 加快了AMP的发现和优化,减少了开发时间和成本.

研究的目的:

为了优化由深度学习算法产生的抗微生物.
使用人工智能和生物信息工具评估修饰的抗菌活性,血液溶解能力和毒性.
合成和验证计算机设计的类对抗细菌病原体和癌细胞的疗效.

主要方法:

利用两个深度学习算法进行初始生成.
采用生物信息学和人工智能工具来预测改性的抗菌活性,血液溶解和毒性.
合成了26种在中产生的,并对细菌菌株,Candida albicans和MCF-7癌细胞进行了体外试验.

主要成果:

确定了26种具有高预测抗微生物活性和安全概况的合成.
九种合成的呈现出低于10μM的最小抑制度 (MIC) 对测试的病原体.
几种体表现出强烈的活性,其中一些达到低至2μM的MIC,六种对乳腺癌细胞表现出有效性.

结论:

由人工智能驱动的优化成功生成了强大的抗微生物.
优化的合成具有显著的抗微生物和抗癌潜力.
这些发现凸显了人工智能在开发下一代抗微生物和治疗剂方面的实用性.

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