HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors.

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
O V Tinkov, V N Osipov, A V Kolotaev, D S Khachatryan, V Y Grigorev
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引用次数: 0

Abstract

Histone deacetylase 6 (HDAC6) is a promising drug target for the treatment of human diseases such as cancer, neurodegenerative diseases (in particular, Alzheimer's disease), and multiple sclerosis. Considerable attention is paid to the development of selective non-toxic HDAC6 inhibitors. To this end, we successfully form a set of 3854 compounds and proposed adequate regression QSAR models for HDAC6 inhibitors. The models have been developed using the PubChem, Klekota-Roth, 2D atom pair fingerprints, and RDkit descriptors and the gradient boosting, support vector machines, neural network, and k-nearest neighbours methods. The models are integrated into the developed HT_PREDICT application, which is freely available at https://htpredict.streamlit.app/. In vitro studies have confirmed the predictive ability of the proposed QSAR models integrated into the HT_PREDICT web application. In addition, the virtual screening performed with the HT_PREDICT web application allowed us to propose two promising inhibitors for further investigations.

HT_PREDICT:基于机器学习的计算开源工具,用于筛选 HDAC6 抑制剂。
组蛋白去乙酰化酶 6(HDAC6)是治疗癌症、神经退行性疾病(尤其是阿尔茨海默病)和多发性硬化症等人类疾病的一个很有前景的药物靶点。开发选择性无毒 HDAC6 抑制剂备受关注。为此,我们成功地形成了一组 3854 种化合物,并为 HDAC6 抑制剂提出了适当的回归 QSAR 模型。这些模型是利用 PubChem、Klekota-Roth、二维原子对指纹和 RDkit 描述因子以及梯度提升、支持向量机、神经网络和 k 最近邻方法建立的。这些模型已集成到开发的 HT_PREDICT 应用程序中,该程序可在 https://htpredict.streamlit.app/ 免费获取。体外研究证实了集成到 HT_PREDICT 网络应用程序中的 QSAR 模型的预测能力。此外,通过 HT_PREDICT 网络应用程序进行的虚拟筛选,我们提出了两种有前景的抑制剂供进一步研究。
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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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