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HT_PREDICT:基于机器学习的计算开源工具,用于选HDAC6抑制剂

O V Tinkov¹, V N Osipov², A V Kolotaev³

¹Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Shevchenko Transnistria State University, Tiraspol, Moldova.

SAR and QSAR in environmental research

|July 15, 2024

概括

研究人员开发了定量结构-活性关系 (QSAR) 模型,以识别选择性激素脱乙酶6 (HDAC6) 抑制剂,用于治疗癌症和神经退行性疾病. 这些模型可以通过HT_PREDICT网络应用程序获得,以帮助药物发现.

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:

基斯脱乙酶6 (HDAC6) 是癌症和神经退行性疾病 (如阿尔茨海默病) 的关键治疗点.
开发有选择性和无毒的HDAC6抑制剂对于有效的治疗策略至关重要.

研究的目的:

创建强大的定量结构-活性关系 (QSAR) 模型来预测HDAC6抑制剂.
将这些QSAR模型集成到一个用户友好的网络应用程序中,用于虚拟选.

主要方法:

利用3854个化合物的数据集,使用PubChem和Klekota-Roth数据与2D原子对指纹和RDKit描述符.
应用机器学习算法,包括梯度提升,支持向量机,神经网络和k-最近邻居.
开发了HT_PREDICT网络应用程序 (https://htpredict.streamlit.app/) 来托管QSAR模型.

主要成果:

建立了经过验证的回归QSAR模型,通过体外研究证实了预测准确度.
该HT_PREDICT应用程序成功执行了化合物的虚拟选.
确定了两种新的,有前途的HDAC6抑制剂,用于未来的临床前研究.

结论:

开发的QSAR模型和HT_PREDICT应用程序为识别潜在的HDAC6抑制剂提供了宝贵的工具.
这种方法加速了与HDAC6失调相关的疾病的药物发现过程.
已识别的化合物需要进一步研究以开发治疗方法.

关键词:

在QSAR中使用QSAR.简化简化简化简化简化剧烈毒性的急性毒性机器学习是机器学习.分子指纹,分子指纹.选择性抑制剂选择性抑制剂

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