Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Jadavpur, Kolkata 700032, India.
Mol Divers. 2012 Aug;16(3):563-77. doi: 10.1007/s11030-012-9388-8. Epub 2012 Aug 14.
γ-Secretase (Gamma Secretase) is a potential drug target in Alzheimer's disease therapeutics. A sequel lead design study was undertaken on a series of bicyclononanes with an aim of identifying potent isofunctional chemotypes. Fragment-based bioisosteric replacement, which considers shape, chemistry, and electrostatics was carried out to mine over four million medicinally relevant fragments of Brood database. The resulting subset, thus, obtained was further mined using consensus QSAR developed from 2D and CoMFA, CoMSIA, GRIND (3D) QSAR predicted endpoints with superior statistical results. The employed consensus prediction and the predicted endpoint values were found to be in good agreement with the experimental values. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed, using the concept of bioisosterism, were found to be promising and could be considered for synthesis and subsequent screening.
γ-分泌酶（Gamma Secretase）是阿尔茨海默病治疗的一个潜在药物靶点。本研究在一系列双环壬烷上进行了后续先导设计研究，旨在确定有效的同功能化学型。基于形状、化学性质和静电相互作用的片段生物等排替换对 Brood 数据库中超过 400 万个药用相关片段进行了挖掘。由此获得的子集进一步使用基于 2D 和 CoMFA、CoMSIA、GRIND（3D）QSAR 的共识 QSAR 进行挖掘，这些 QSAR 预测端点具有优越的统计学结果。所采用的共识预测和预测端点值与实验值吻合良好。使用不同的统计指标对生成的模型进行了验证，并且进行了基于相似性的覆盖估计，以定义适用性边界。使用生物等排概念设计的一些类似物具有很大的潜力，可考虑用于合成和后续筛选。
