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针对Trypanosoma cruzi的潜在治疗方法进行二维QSAR驱动的虚拟查

Naseer Maliyakkal¹, Sunil Kumar², Ratul Bhowmik³

¹Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Khamis Mushait, Saudi Arabia.

Frontiers in chemistry

|June 26, 2025

概括

研究人员开发了一种机器学习模型,以发现针对全球健康威胁的查加斯病 (CD) 的新药. 该模型确定了有前途的抑制剂F6609-0134,推动了寻找有效的抗查加斯疗法的研究.

科学领域:

计算化学计算化学
药物发现药物发现药物发现
机器学习机器学习

背景情况:

查加斯病 (CD) 是由*Trypanosoma cruzi*引起的,在全球范围内影响着数百万人,并且由于移民而蔓延.
消除CD的当前挑战包括有限的认识,诊断和医疗保健准入.
开发更安全,更有效的抗查加斯疗法是一个至关重要的未满足的需求.

研究的目的:

开发一个强大的机器学习驱动的定量结构-活性关系 (ML-QSAR) 模型,用于预测*Trypanosoma cruzi*抑制剂.
为了加速新型抗查加斯疗法的药物发现过程.
为了识别和验证查加斯病的潜在候选药物.

主要方法:

从ChEMBL数据库中整理了1183种*Trypanosoma cruzi*抑制剂的数据集.
计算了分子描述符,并应用了特征选择.
支持矢量机 (SVM),人工神经网络 (ANN) 和随机森林 (RF) 模型被开发和优化.
使用CDK指纹的ANN-QSAR模型显示了最高的预测性能.
大型化学图书馆使用ANN-QSAR模型和ADMET过进行了选.
为了验证,进行了分子对接,分子动力学模拟和自由能量分析.

主要成果:

由ANN驱动的QSAR模型实现了高预测准确性 (培训时的皮尔森相关系数为0.9874,测试时为0.6872).
确定了12种具有pIC₅₀ ≥5的潜在抑制剂.
从分子对接研究中,F6609-0134成为了最成功的分子.
分子动力学模拟和自由能分析证实了F6609-0134.4的稳定性和高结合亲和力.

结论:

开发的ML-QSAR模型是加速发现*Trypanosoma cruzi*抑制剂的强大工具.
F6609-0134显示出作为用于治疗查加斯病的化合物的显著潜力.
需要对F6609-0134进行进一步的评估,以使其成为可行的抗查加斯疗法.

关键词:

查加斯病是查加斯病的一种疾病.试osoma cruzi 是一个类型的虫.人工神经网络的人工神经网络机器学习是机器学习.分子对接的分子对接.分子动力学分子动力学定量结构活动关系的量化结构.虚拟选虚拟选虚拟选

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