Erös Dániel, Kéri György, Kövesdi István, Szántai-Kis Csaba, Mészáros György, Orfi László
Semmelweis University, Department of Medicinal Chemistry, Biopeptide Research Group of the Hungaian Academy of Sciences, Budapest, Hungary.
Mini Rev Med Chem. 2004 Feb;4(2):167-77. doi: 10.2174/1389557043487466.
ADME/Tox computational screening is one of the most hot topics of modern drug research. About one half of the potential drug candidates fail because of poor ADME/Tox properties. Since the experimental determination of water solubility is time-consuming also, reliable computational predictions are needed for the pre-selection of acceptable "drug-like" compounds from diverse combinatorial libraries. Recently many successful attempts were made for predicting water solubility of compounds. A comprehensive review of previously developed water solubility calculation methods is presented here, followed by the description of the solubility prediction method designed and used in our laboratory. We have selected carefully 1381 compounds from scientific publications in a unified database and used this dataset in the calculations. The externally validated models were based on calculated descriptors only. The aim of model optimization was to improve repeated evaluations statistics of the predictions and effective descriptor scoring functions were used to facilitate quick generation of multiple linear regression analysis (MLR), partial least squares method (PLS) and artificial neural network (ANN) models with optimal predicting ability. Standard error of prediction of the best model generated with ANN (with 39-7-1 network structure) was 0.72 in logS units while the cross validated squared correlation coefficient (Q(2)) was better than 0.85. These values give a good chance for successful pre-selection of screening compounds from virtual libraries, based on the predicted water solubility.
药物代谢动力学/药物毒性（ADME/Tox）计算筛选是现代药物研究中最热门的话题之一。大约一半的潜在药物候选物因ADME/Tox性质不佳而失败。由于实验测定水溶性也很耗时，因此需要可靠的计算预测来从各种组合文库中预选可接受的“类药物”化合物。最近，在预测化合物水溶性方面进行了许多成功的尝试。本文对先前开发的水溶性计算方法进行了全面综述，随后描述了我们实验室设计和使用的溶解度预测方法。我们从科学出版物中精心挑选了1381种化合物，统一存入数据库，并将该数据集用于计算。外部验证模型仅基于计算得到的描述符。模型优化的目的是改善预测的重复评估统计数据，并使用有效的描述符评分函数来促进快速生成具有最佳预测能力的多元线性回归分析（MLR）、偏最小二乘法（PLS）和人工神经网络（ANN）模型。用人工神经网络（具有39-7-1网络结构）生成的最佳模型的预测标准误差在logS单位下为0.72，而交叉验证平方相关系数（Q(2)）优于0.85。基于预测的水溶性，这些值为从虚拟文库中成功预选筛选化合物提供了良好机会。
