Kumar Sivakumar Prasanth, Jha Prakash C, Jasrai Yogesh T, Pandya Himanshu A
a Department of Bioinformatics , Applied Botany Centre (ABC), University School of Sciences, Gujarat University , Ahmedabad 380 009 , India.
b School of Chemical Sciences, Central University of Gujarat , Sector-30, Gandhinagar 382030 , India.
J Biomol Struct Dyn. 2016;34(3):540-59. doi: 10.1080/07391102.2015.1044474. Epub 2015 May 21.
The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.
估计小分子的原子部分电荷以计算分子相互作用场（MIFs）是基于场的定量构效关系（QSAR）中的一个重要过程。多项研究表明部分电荷方案的影响，其会极大地影响QSAR模型的预测准确性，并聚焦于选择能提供最高交叉验证相关系数（[公式：见原文]或q(2)）的合适电荷模型，以解释化学结构相对于生物学终点的变化。本研究将这一重点转向理解在各种电荷模型中被认为可解释构效关系的分子区域，并识别与活性相关的分子区域的共识图景。我们选择了11个不同的数据集，并使用包括Gasteiger - Marsili、Del Re、默克分子力场、Hückel、Gasteiger - Hückel和Pullman在内的各种电荷方案开发了基于MIF的QSAR模型。然后将广义所得QSAR模型与Open3DQSAR模型进行比较，以果断地解释MIF描述符。我们建议通过研究各种基于电荷的模型来精确理解构效关系，从而有效地确定活性贡献或优化区域。
