Michielan Lisa, Bacilieri Magdalena, Kaseda Chosei, Moro Stefano
Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy.
Bioorg Med Chem. 2008 May 15;16(10):5733-42. doi: 10.1016/j.bmc.2008.03.064. Epub 2008 Mar 30.
Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.
已经探索了几种定量结构-性质关系(QSPR)方法来预测水溶性或水合溶剂化自由能ΔG(sol),这是影响化合物药代动力学特征和毒性的关键参数。人们大多认为,水合溶剂化自由能可以根据溶质分子表面静电势的性质进行定量表达。在本研究中,我们引入了自相关分子静电势(autoMEP)向量,并结合非线性响应面分析(RSA)作为一种替代的3D-QSPR策略,以评估有机化合物的水合溶剂化自由能。通过使用248种有机化学品的集合,获得了一个稳健的QSPR模型(r(cv)=0.93)。基于23个分子的外部测试集证实了autoMEP/RSA模型具有良好的预测能力,表明其在大型有机化合物库的水溶性计算机模拟预测中具有进一步的适用性。