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Combinatorial Library Enumeration and Lead Hopping using Comparative Interaction Fingerprint Analysis and Classical 2D QSAR Methods for Seeking Novel GABA(A) alpha(3) Modulators

机译:使用比较相互作用指纹分析和经典二维QSAR方法寻找新的GABA(A)alpha(3)调节剂的组合库枚举和铅跳跃

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Selective modulators of GABA(A) alpha(3) (gamma amino butyric acid alpha(3)) receptor are known to alleviate the side effects associated with nonspecific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using the combichem approach endorsed mining in a lead-like chemical space. Primary screening of the massive library was undertaken using the "Miscreen" toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset, thus, obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (comparative interaction fingerprint analysis). CoIFA encodes protein-ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABA(A) alpha(3) receptor, standard comparative modeling techniques were employed to construct a homology model of GABA(A) alpha(3) receptor. A typical docking study was then carried out on the modeled structure, and the interaction fingerprints generated based on the docked binding mode were used to derive propensity values for the interacting atom pairs that served as pseudo-energy variables to generate a CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus two-dimensional QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. 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.
机译:已知GABA(A)alpha(3)(γ氨基丁酸alpha(3))受体的选择性调节剂可减轻与非特异性调节剂有关的副作用。对一系列功能选择性的酞嗪进行了后续研究,其意识形态是确定更有效的同功能化学型。使用combichem方法枚举的生物立体数据库支持在类似铅的化学空间中进行开采。使用“ Miscreen”工具箱对大型图书馆进行了初步筛选,该工具箱使用了复杂的贝叶斯统计数据来计算生物活性得分。因此,使用新的蛋白质化学计量学方法对所得的子集进行了挖掘,该方法结合了分子对接和QSAR形式,称为CoIFA(比较相互作用指纹分析)。 CoIFA基于统计推断将蛋白质-配体相互作用项编码为倾向值,以构建有助于在虚拟筛选过程中进行决策的分类QSAR模型。在缺少GABA(A)alpha(3)受体的实验解析结构的情况下,采用标准的比较建模技术来构建GABA(A)alpha(3)受体的同源性模型。然后,对建模的结构进行了典型的对接研究,并基于对接的结合模式生成的相互作用指纹用于推导用作伪能量变量的相互作用原子对的倾向值,以生成CoIFA模型。使用从混淆矩阵得出的不同指标来验证CoIFA模型的分类准确性。使用共识二维QSAR方法进行了进一步的预测线索挖掘,与任意选择单个QSAR模型相比,该方法提供了更好的预测协议。使用不同的统计指标验证了生成模型的预测能力,并进行了基于相似度的覆盖率估计以定义适用性边界。很少有人发现使用生物立体异构概念设计的类似物很有前途,可以考虑用于合成和随后的筛选。

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