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Abelson 酪氨酸蛋白激酶 1 作为抗白血病药物发现的主要靶点。当前计算机辅助药物设计方法的作用。

Abelson tyrosine-protein kinase 1 as principal target for drug discovery against leukemias. Role of the current computer-aided drug design methodologies.

机构信息

REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.

出版信息

Curr Top Med Chem. 2012;12(24):2745-62. doi: 10.2174/1568026611212240005.

Abstract

The discovery of anti-cancer agents is an area which continues in accelerated expansion. Leukemias (Lkms) are among the most investigated cancers due to its high and dominant prevalence in children. Computer-aided drug design (CADD) methodologies have been extremely important for the discovery of potent anti-Lkms agents, providing essential insights about the molecular patterns which could be involved in the appearance and development of anti-Lkms activity. The present review is focused on the role of the current CADD methodologies for the discovery of anti-Lkms agents with strong emphasis on the in silico prediction of inhibitors against the primary protein associated with the appearance of Lkms: Abelson tyrosine-protein kinase 1 (TPK-ABL1). In order to make a contribution to the field, we also developed a unified ligand-based approach by exploring Quantitative-Structure Activity Relationships (QSAR) studies. Here, we focused on the construction of two multi-targets (mt) QSAR models by employing a large and heterogeneous database of compounds. These models exhibited excellent statistical quality and predictive power to classifying more than 92% of inhibitors/ no inhibitors against seven proteins associated with Lkms, in both training and prediction sets. By using our unified ligand-based approach we identified several fragments as responsible for the anti-Lkms activity through inhibition of proteins, and new molecules were suggested as versatile inhibitors of the seven proteins under study.

摘要

抗癌药物的发现是一个持续加速发展的领域。白血病(Lkms)是研究最多的癌症之一,因为它在儿童中发病率高且占主导地位。计算机辅助药物设计(CADD)方法对于发现有效的抗 Lkms 药物非常重要,为可能参与抗 Lkms 活性出现和发展的分子模式提供了重要的见解。本综述重点介绍了当前 CADD 方法在发现抗 Lkms 药物中的作用,特别强调了针对与 Lkms 出现相关的主要蛋白——Abelson 酪氨酸蛋白激酶 1(TPK-ABL1)的抑制剂的计算机预测。为了为该领域做出贡献,我们还通过探索定量构效关系(QSAR)研究开发了一种统一的基于配体的方法。在这里,我们专注于通过使用大量异构化合物数据库构建两个多靶(mt)QSAR 模型。这些模型在训练集和预测集上都表现出了极好的统计质量和预测能力,能够对超过 92%的针对七种与 Lkms 相关的蛋白的抑制剂/非抑制剂进行分类。通过使用我们的统一基于配体的方法,我们确定了几个片段通过抑制蛋白具有抗 Lkms 活性,并且提出了新的分子作为所研究的七种蛋白的多功能抑制剂。

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