QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.
QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.
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DOI：
10.1016/j.bmc.2005.12.036
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发表时间：
2006-05
影响因子：
3.5
通讯作者：
F. Zheng;E. Bayram;Sangeetha P. Sumithran;Joshua T. Ayers;C. Zhan;J. Schmitt;L. Dwoskin;P. Crooks
中科院分区：
文献类型：
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作者：
F. Zheng;E. Bayram;Sangeetha P. Sumithran;Joshua T. Ayers;C. Zhan;J. Schmitt;L. Dwoskin;P. Crooks
反向传播人工神经网络（ANN）的数据集上训练的42个分子的定量IC 50值模型的结构-活性关系的单和双季铵盐作为拮抗剂在神经元烟碱乙酰胆碱受体（nAChR）介导尼古丁诱发的多巴胺释放。ANN QSAR模型在实验和计算的log（1/IC 50）（r2=0.76，rcv 2 =0.64）之间产生了合理水平的相关性。对IC 50值>1μM的18个分子的数据集进行了模型的外部测试。其中14个被正确分类。各种模型，包括自组织映射（SOM），所有60个分子的分类能力也进行了评估。对建模结果的详细分析揭示了所用描述符对经训练的ANN QSAR模型的以下相对贡献：与季铵头基相连的N-烷基链的长度约为44.0%，分子的Moriguchi辛醇-水分配系数约为20.0%，分子表面积约为13.0%，第一组分形状方向WHIM指数/未加权占12.6%，Ghose-Crippen摩尔活度占7.8%，最低未占分子轨道能量占2.6%。ANN QSAR模型也进行了评价，使用一组13个新合成的化合物（11个生物活性拮抗剂和两个生物活性化合物），其结构以前没有被利用在训练集中。预测13种化合物中有12种具有活性，这进一步支持了训练模型的稳健性。从建模中获得的其他见解包括双喹啉系列中的结构修饰，涉及用氮原子取代5和/或8以及5′和/或8′碳原子，预测非活性化合物。这些数据可以有效地用于通过从用于合成的候选分子库中消除预测低活性的化合物来降低合成和体外筛选活性。人工神经网络QSAR模型的应用已导致在本研究中成功地发现了六个新化合物，其在负责介导尼古丁诱发的多巴胺释放的nAChR亚型上的实验IC 50值小于0.1μM，表明人工神经网络QSAR模型是药物发现的有价值的辅助工具。
Back-propagation artificial neural networks (ANNs) were trained on a dataset of 42 molecules with quantitative IC50values to model structure–activity relationships of mono- and bis-quaternary ammonium salts as antagonists at neuronal nicotinic acetylcholine receptors (nAChR) mediating nicotine-evoked dopamine release. The ANN QSAR models produced a reasonable level of correlation between experimental and calculated log(1/IC50) (r2=0.76, rcv2=0.64). An external test for the models was performed on a dataset of 18 molecules with IC50values >1μM. Fourteen of these were correctly classified. Classification ability of various models, including self-organizing maps (SOM), for all 60 molecules was also evaluated. A detailed analysis of the modeling results revealed the following relative contributions of the used descriptors to the trained ANN QSAR model: ∼44.0% from the length of the N-alkyl chain attached to the quaternary ammonium head group, ∼20.0% from Moriguchi octanol–water partition coefficient of the molecule, ∼13.0% from molecular surface area, ∼12.6% from the first component shape directional WHIM index/unweighted, ∼7.8% from Ghose–Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy. The ANN QSAR models were also evaluated using a set of 13 newly synthesized compounds (11 biologically active antagonists and two biologically inactive compounds) whose structures had not been previously utilized in the training set. Twelve among 13 compounds were predicted to be active which further supports the robustness of the trained models. Other insights from modeling include a structural modification in the bis-quinolinium series that involved replacing the 5 and/or 8 as well as the 5′ and/or 8′ carbon atoms with nitrogen atoms, predicting inactive compounds. Such data can be effectively used to reduce synthetic and in vitro screening activities by eliminating compounds of predicted low activity from the pool of candidate molecules for synthesis. The application of the ANN QSAR model has led to the successful discovery of six new compounds in this study with experimental IC50values of less than 0.1μM at nAChR subtypes responsible for mediating nicotine-evoked dopamine release, demonstrating that the ANN QSAR model is a valuable aid to drug discovery.