New and original pKa prediction method using grid molecular interaction fields
New and original pKa prediction method using grid molecular interaction fields
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DOI：
10.1021/ci700018y
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发表时间：
2007-11-01
影响因子：
5.6
通讯作者：
Cruciani, Gabriele
中科院分区：
文献类型：
--
作者：
Milletti, Francesca;Storchi, Loriano;Cruciani, Gabriele
分子最重要的物理化学性质之一是pK（a）。已知ADME谱分析中的两个必要参数溶解度和亲脂性受pK（a）控制，并且受体结合可受pK（a）影响。由于大多数药物在生理条件下是电离的，pK（a）与药物化学特别相关。尽管在高通量测量方面取得了许多进展，但计算机测定仍然是获得pK（a）的最快和最便宜的方法。本文提出了一种新的计算有机化合物pK（a）的方法。使用程序GRID生成描述符，并且这些描述符基于在一组分子片段上预先计算的分子相互作用场。新方法通过使用24 617 pK（a）值的大型多样化数据集进行开发，训练和交叉验证。本文给出了421个酸性含氮化合物（RMSE = 0.41，r（2）= 0.97，q（2）= 0.87）和947个六元氮杂环碱（RMSE = 0.60，r（2）= 0.93，q（2）= 0.85）的计算结果。对于外部验证，选择了28种新化合物，涵盖了9种不同的可电离基团，39个pK（a）值可以通过光谱梯度分析（SGA）实验确定。实验结果与计算结果的比较表明，该方法的预测能力较好（外集，r（2）= 0.85，RMSE = 0.90）。
One of the most important physicochemical properties of a molecule is pK(a). It is known that two parameters imperative in ADME profiling, solubility, and lipophilicity are governed by pK(a), and receptor binding can be influenced by pK(a). Because most drugs are ionized in physiological conditions, pK(a) is particularly relevant to medicinal chemistry. Despite the numerous advances in high-throughput measurements, in silico determination is still the fastest and cheapest way of obtaining pK(a). This paper presents a new original computational method for pK(a) prediction of organic compounds. Descriptors were generated using the program GRID, and these descriptors are based on molecular interaction fields precomputed on a set of molecular fragments. The new method was developed, trained, and cross-validated by using a large and diverse data set of 24 617 pK(a) values. This paper presents the results for a class of 421 acidic nitrogen compounds (RMSE = 0.41, r(2) = 0.97, q(2) = 0.87) and for a class of 947 six-membered N-heterocyclic bases (RMSE = 0.60, r(2) = 0.93, q(2) = 0.85). For external validation 28 novel compounds were selected that covered nine different ionizable groups, and 39 pK(a) values could be experimentally determined by spectral gradient analysis (SGA). Comparison of experimental pKa with calculated pK(a) demonstrated that the predictive ability of the method is good (external set, r(2) = 0.85, RMSE = 0.90).