Khedkar Vijay M, Coutinho Evans C
Department of Pharmaceutical Chemistry, Bombay College of Pharmacy , Kalina, Santacruz (E), Mumbai 400098, India.
J Chem Inf Model. 2015 Jan 26;55(1):194-205. doi: 10.1021/ci5006367. Epub 2015 Jan 12.
Molecular similarity methods have played a crucial role in the success of structure-based and computer-assisted drug design. However, with the exception of CoMSIA, the current approaches for estimating molecular similarity yield a global picture thereby providing limited information about the local spatial molecular features responsible for the variation of activity with the 3D structure. Application of molecular similarity measures, each related to the functional "pieces" of a ligand-receptor complex, is advantageous over a composite molecular similarity alone and will provide more insights to rationally interpret the activity based on the receptor and ligand structural features. Building on the ideas of our previously published methodologies-CoRIA and LISA, we present here a local molecular similarity based receptor dependent QSAR method termed CoRILISA which is a hybrid of the two approaches. The method improves on previous techniques by inclusion of receptor attributes for the calculation and comparison of similarity between molecules. For validation studies, the CoRILISA methodology was applied on three large and diverse data sets-glycogen phosphorylase b (GPb), human immunodeficiency virus-1 protease (HIV PR), and cyclin dependent kinase 2 (CDK2) inhibitors. The statistics of the CoRILISA models were benchmarked against the standard CoRIA approach and with other published approaches. The CoRILISA models were found to be significantly better, especially in terms of the predictivity for the test set. CoRILISA is able to identify the thermodynamic properties associated with residues that define the active site and modulate the variation in the activity of the molecules. It is a useful tool in the fragment-based drug discovery approach for ligand activity prediction.
分子相似性方法在基于结构的计算机辅助药物设计的成功中发挥了关键作用。然而，除了CoMSIA之外，目前估计分子相似性的方法给出的是全局情况，因此关于导致活性随三维结构变化的局部空间分子特征的信息有限。应用与配体 - 受体复合物的功能“片段”相关的分子相似性度量，比单独的复合分子相似性更具优势，并且将提供更多见解，以便基于受体和配体结构特征合理地解释活性。基于我们之前发表的方法CoRIA和LISA的理念，我们在此提出一种基于局部分子相似性的受体依赖性QSAR方法，称为CoRILISA，它是这两种方法的混合体。该方法通过纳入受体属性来计算和比较分子间的相似性，对先前的技术进行了改进。为了进行验证研究，CoRILISA方法应用于三个大型且多样的数据组——糖原磷酸化酶b（GPb）、人类免疫缺陷病毒1蛋白酶（HIV PR）和细胞周期蛋白依赖性激酶2（CDK2）抑制剂。CoRILISA模型的统计数据与标准CoRIA方法以及其他已发表的方法进行了对比。结果发现CoRILISA模型明显更好，尤其是在对测试集的预测性方面。CoRILISA能够识别与定义活性位点并调节分子活性变化的残基相关的热力学性质。它是基于片段的药物发现方法中用于预测配体活性的有用工具。
