Melo-Filho Cleber Camilo, Braga Rodolpho Campos, Andrade Carolina Horta
LabMol, Faculdade de Farmacia, Universidade Federal de Goias, Rua 240, Qd. 87, Setor Leste Universitario, Goiania, GO, 74605-170, Brazil.
Curr Comput Aided Drug Des. 2014;10(2):148-59. doi: 10.2174/1573409910666140410111043.
Drug discovery is mostly guided by innovative and knowledge by the application of experimental and computational approaches. Quantitative structure-activity relationships (QSAR) have a critical task in the discovery and optimization of lead compounds, thereby contributing to the development of new chemical entities. 3D-QSAR methods use the information of the tridimensional molecular structure of ligands and can be applied to elucidate the relationships between 3D molecular interactions and their measured biological property, therefore, providing a rational approach for the development of new potential compounds. The purpose of this review is to provide a perspective of the utility of 3DQSAR approaches in drug design, focusing on progress, challenges and future orientations. The essential steps involved to generate reliable and predictive CoMFA models are discussed. Moreover, we present an example of application of a CoMFA study to derive 3D-QSAR models for a series of oxadiazoles inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR).
药物发现主要由创新和知识引导，通过应用实验和计算方法来实现。定量构效关系（QSAR）在先导化合物的发现和优化中起着关键作用，从而有助于新化学实体的开发。三维定量构效关系（3D-QSAR）方法利用配体三维分子结构的信息，可用于阐明三维分子相互作用与其测得的生物学性质之间的关系，因此为开发新的潜在化合物提供了一种合理的方法。本综述的目的是提供3D-QSAR方法在药物设计中的应用前景，重点关注进展、挑战和未来方向。讨论了生成可靠且具有预测性的比较分子场分析（CoMFA）模型所涉及的基本步骤。此外，我们展示了一个CoMFA研究应用实例，以推导针对曼氏血吸虫硫氧还蛋白谷胱甘肽还原酶（SmTGR）的一系列恶二唑抑制剂的3D-QSAR模型。
