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可解释的支载体分类器可用于可靠地预测改性对大肠杆菌的抗菌活性

Remmer L Salas¹, Portia Mahal G Sabido², Ricky B Nellas³

¹Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City, 1101, Philippines; Department of Natural Sciences and Mathematics, Visayas State University, Tolosa, Leyte, 6503, Philippines.

Journal of molecular graphics & modelling

|October 10, 2025

概括

我们开发了ISCAPE,这是一种机器学习模型,可以预测抗菌 (AMP) 对大肠杆菌的抗菌活性. ISCAPE准确地识别了有效的天然和改性,加速了对新抗生素的研究.

科学领域:

计算化学的计算化学
机器学习在药物发现中的应用
抗菌研究抗菌研究

背景情况:

抗菌素耐药性 (AMR) 的上升需要超越传统抗生素的新型治疗策略.
抗微生物 (AMP) 显示出作为替代品的希望,但它们的发现是具有挑战性的.
现有的AMP预测模型在范围和解释性方面存在局限性.

研究的目的:

开发一种可解释的机器学习 (ML) 模型,ISCAPE,用于预测类对大肠杆菌的抗菌活性.
仅使用SMILES字符串,能够预测天然和化学修饰的.
增强新型AMP的发现管道.

主要方法:

开发了ISCAPE,这是一个支持载体分类器,其训练基于针对大肠杆菌ATCC 25922 (MIC ≤16μg/mL) 的AMP活性精选数据集.
使用简化分子输入线输入系统 (SMILES) 字符串进行体表示.
为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

ISCAPE实现了高性能,超越了最先进的模型,其AUROC为91.83%和MCC为71.86%.
关键的预测特征包括碳-碳对分数和扩展连接指纹 (ECFP).
SHAP分析确定了驱动抗菌活性的关键分子特征.

结论:

ISCAPE是第一个可解释的ML预测器,用于对特定大肠杆菌菌株的天然和改性活性.
该模型通过精确确定关键分子特征,促进新型AMP的设计,减少了广泛的结构-活性关系研究.
ISCAPE提供了一个用户友好的工具来加速AMP发现和打击AMR.

关键词:

抗微生物是一种抗微生物.抗微生物耐药性抗微生物耐药性人工智能的人工智能是人工智能.药物发现药物发现机器学习机器学习分子指纹是分子指纹.

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