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使用先进的机器学习技术预测Anthrapyrazole衍生物的抗瘤活性

Marcin Gackowski¹, Robert Pluskota¹, Marcin Koba¹

¹Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, PL-85089, Bydgoszcz, Poland.

Current computer-aided drug design

|June 13, 2023

概括

新的定量结构-活性关系 (QSAR) 模型预测了antrapyrazole类似物的抗瘤活性. 人工神经网络 (ANN) 在开发新的抗癌药物方面表现出卓越的可预测性.

科学领域:

药用化学医学化学
计算化学计算化学
药物发现药物发现药物发现

背景情况:

人类pyrazoles代表了一类新的抗瘤药物.
它们是环素的继承者,具有广泛的抗瘤活性.
这一类在各种模型瘤中表现有前途.

研究的目的:

开发新的定量结构-活动关系 (QSAR) 模型.
为了预测 antrapyrazole 类型的抗瘤活性.
确定影响抗癌效应的关键结构特征.

主要方法:

评估了四种机器学习算法:人工神经网络 (ANN),增强树,多变量自适应回归线 (MARS) 和随机森林.
使用可预测性,精度和准确性等验证指标评估预测性性能.
使用多层感知子 (MLP)-15-7-1网络进行预测.

主要成果:

ANN和增强树算法满足了预测抗癌效应的验证标准.
确定ANN方法,特别是MLP-15-7-1网络是首选的算法.
观察到预测和实验pIC50值之间的高度相关性,ANN显示最低的平均绝对误差.
敏感性分析确定了活动的关键结构特征.

结论:

该ANN战略有效地整合了地形和拓信息.
这种方法可以指导新型Anthrapyrazole类似物的设计和开发.
ANN模型为发现新的抗癌分子提供了一个有前途的工具.

关键词:

人类pyrazoles的使用.它具有抗瘤活性.人工神经网络的人工神经网络推动了树木的生长.多变量自适应回归线 (splines) 是多变量自适应回归线.随机的森林. 随机的森林.

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