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Predicting pKa Values in Aqueous Solution for the Guanidine Functional Group from Gas Phase Ab Initio Bond Lengths

机译:从气相开始的键长预测胍基官能团的水溶液中的pKa值

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Here we applied a novel method to predict pKa values of the guanidine functional group, which is a notoriously difficult. This method, which was developed in our lab, uses only one ab initio bond length obtained at a low level of theory. The method is shown to work for drug molecules, delivers prediction errors of less than 0.5 log units, successfully treats tautomerisation in close relation with experiment, and demonstrates strong correlations with only a few data points. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets. One then observes that molecules within a subset possess a common substructure. Each high correlation subset exists in its own region of chemical space. The high correlation subset method is explored with respect to this position in chemical space, in particular tautomerisation. The proposed method is able to distinguish between different tautomeric forms and the preferred tautomeric form emerges naturally, in agreement with experiment. 1 Introduction Knowing the extent to which a compound donates or accepts a proton, or what its preferred protonated state is for a given system of known pH is vital in both chemistry and biological contexts. The pharmacokinetic properties and aqueous solubility of a molecule are hugely reliant on the protonation state of a molecule. A drug molecule is usually required to pass through a membrane to reach its target site. Ionic species have great difficulty permeating through the membrane whereas neutral molecules can be more readily absorbed. Once the target is reached, the protonation state of the molecule will in most cases determine the nature of the binding to the active site of the protein. If a molecule is not in the correct ionic state it may not be delivered to the target site, bind to the correct active site or generate the correct action from the protein. In summary, the molecule must satisfy these three stages for successful activity. The physiological pH of the body is 7.4; therefore, knowing a molecule's pKa allows for the determination of its ionic state in physiological conditions. Agrochemically relevant molecules suffer the same fates as drug molecules with respect to transportation, binding and activity for the target within the plant or organism. Indeed, 85% of current herbicides are weak acids. Biological molecules with a pKa deviating more than two units from the target's pKa tend not to be active. In that case the molecule will fail in at least one of the following: absorption by the organism, transportation to the correct site, binding to the site, or initiation of the desired action.
机译:在这里,我们应用了一种新颖的方法来预测胍基官能团的pKa值,这是非常困难的。这种方法是在我们的实验室中开发的,仅使用了从低水平获得的从头算起的键长。该方法显示出可用于药物分子的方法,预测误差小于0.5 log个单位,成功地与实验密切相关地处理了互变异构,并且仅与几个数据点就显示出强相关性。从头算起键长度的高结构含量使给定的数据集从本质上将自身划分为高相关子集。然后观察到一个子集中的分子具有共同的子结构。每个高相关子集都存在于其自己的化学空间区域中。关于化学空间中的该位置,特别是互变异构,探索了高相关子集方法。所提出的方法能够区分不同的互变异构形式,并且与实验一致,优选的互变异构形式自然出现。 1引言在化学和生物学背景下,了解化合物给予或接受质子的程度,或给定已知pH值的系统的质子化状态是什么都至关重要。分子的药代动力学性质和水溶性极大地依赖于分子的质子化状态。通常需要药物分子穿过膜才能到达其靶位。离子种类很难透过膜渗透,而中性分子则更易于吸收。一旦达到目标,分子的质子化状态将在大多数情况下决定与蛋白质活性位点结合的性质。如果分子不是处于正确的离子状态,则可能无法传递至目标位点,无法结合至正确的活性位点或无法从蛋白质产生正确的作用。总之,该分子必须满足这三个阶段的成功活性。人体的生理pH值为7.4;因此,知道分子的pKa可以确定其在生理条件下的离子状态。在植物或生物体内靶标的转运,结合和活性方面,与农业化学有关的分子遭受与药物分子相同的命运。实际上,目前的除草剂中有85%是弱酸。 pKa与靶标pKa偏离两个以上单位的生物分子往往没有活性。在这种情况下,分子将失败,至少发生以下情况之一:被生物体吸收,转运到正确的位点,结合到该位点或引发所需的作用。

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