In silico evaluation of logD_7.4 and comparison with other prediction methods

Abstract

Lipophilicity, evaluated by either n-octanol/water partition coefficient or n-octanol/buffer solution distribution coefficient, is of high importance in pharmacology, toxicology, and medicinal chemistry. A quantitative structureproperty relationship study was carried out to predict distribution coefficients at pH 7.4 (logD 7.4 ) of a large data set consisting of 1130 organic compounds. Partial least squares and support vector machine (SVM) regressions were employed to build prediction models with 30 molecular descriptors selected by genetic algorithm. The obtained results demonstrated that the SVM model is more reliable and has a better prediction performance than the partial least squares model. The square correlation coefficients of fitting, cross validation, and prediction are 0.92, 0.90, and 0.89, respectively. The corresponding root mean square errors are 0.52, 0.59, and 0.56, respectively. The robustness, reliability, and generalization ability of the model were assessed by Y-randomization test and applicability domain. When compared with logD 7.4 values calculated by five existing methods from Discovery Studio and ChemAxon, our SVM model shows superiority over them. The results indicated that our model could give a reliable and robust prediction of logD 7.4 .

Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease

“…In this part, we mainly evaluated the molecular weight and two important ADME (absorption, distribution, metabolism, elimination) properties for druggability by corresponding QSAR models developed by our group: logD 7.4 (the distribution coefficients at pH = 7.4) (Wang J. B. et al, 2015 ; Wang et al, 2017 ), and logPapp (the Caco-2 membrane permeability) (Wang et al, 2016 ). Based on previous studies, a good drug candidate should have a logD 7.4 value smaller than 5, a logPapp value larger than −5.15 and a molecular weight smaller than 500.…”

Chemometrics in instrumental analysis of complex systems—in honor and memory of Yi‐Zeng Liang

“…In Liang's group, they introduced machine learning techniques into QSAR studies such as the kernel k‐nearest neighbor algorithm, particle swarm optimization‐combined multiple linear regression, and kernel fusion‐based support vector machine. In these works, they studied the relationships between molecular structures and toxicity, logD 7.4 , the inhibitory effect, and water solubility for a set of structurally diverse drugs …”

Automated support vector regression

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