Computation of Octanol−Water Partition Coefficients by Guiding an Additive Model with Knowledge

State Key Laboratory for Structural Chemistry of Unstable and Stable Species , Peking University, Peping, Beijing, China
Journal of Chemical Information and Modeling (Impact Factor: 3.74). 11/2007; 47(6):2140-8. DOI: 10.1021/ci700257y
Source: PubMed


We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.

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Available from: Tiejun Cheng, Oct 02, 2015
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    • "Compounds with Tanimoto coefficients of less than 0.4 with any of the selected HIV-1 protease inhibitors were selected. Second, six physical properties including molecular weight (MW), number of hydrogen-bond donors and hydrogen-bond acceptors, number of rotatable bonds, logP, number of rings, and number of N/O atoms were calculated using XLOGP3 [32] for the HIV-1 proteases inhibitors and compounds selected from the previous step. Then, compounds with calculated physical properties similar to any of the HIV-1 protease inhibitors were further selected. "
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    ABSTRACT: The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protease. The method sequentially applied SVM (Support Vector Machine), shape similarity, pharmacophore modeling and molecular docking. Using a validation set (270 positives, 155,996 negatives), the multistage virtual screening method showed a high hit rate and high enrichment factor of 80.47% and 465.75, respectively. Furthermore, this approach was applied to screen the National Cancer Institute database (NCI), which contains 260,000 molecules. From the final hit list, 6 molecules were selected for further testing in an in vitro HIV-1 protease inhibitory assay, and 2 molecules (NSC111887 and NSC121217) showed inhibitory potency against HIV-1 protease, with IC50 values of 62 μM and 162 μM, respectively. With further chemical development, these 2 molecules could potentially serve as HIV-1 protease inhibitors. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
    European Journal of Medicinal Chemistry 07/2015; 101:409-418. DOI:10.1016/j.ejmech.2015.06.054 · 3.45 Impact Factor
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    • "In fact, many research works have been done to do such predictions. For example, the octanol/water partition coefficient (P O/W ) has been accurately calculated and predicted [18] [19]. However, the prediction of solute partition coefficient in CCC multi-solvent mixture is a more complex operation. "
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    ABSTRACT: Solvent system selection is the first step toward a successful counter-current chromatography (CCC) separation. This paper introduces a systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model, which is efficient in predicting the solute partition coefficient. Firstly, the application of the NRTL-SAC method was extended to the ethyl acetate/n-butanol/water and chloroform/methanol/water solvent system families. Moreover, the versatility and predictive capability of the NRTL-SAC method were investigated. The results indicate that the solute molecular parameters identified from hexane/ethyl acetate/methanol/water solvent system family are capable of predicting a large number of partition coefficients in several other different solvent system families. The NRTL-SAC strategy was further validated by successfully separating five components from Salvia plebeian R.Br. We therefore propose that NRTL-SAC is a promising high throughput method for rapid solvent system selection and highly adaptable to screen suitable solvent system for real-life CCC separation. Copyright © 2015. Published by Elsevier B.V.
    Journal of Chromatography A 03/2015; 1393. DOI:10.1016/j.chroma.2015.03.016 · 4.17 Impact Factor
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    • "Furthermore, the diffusion of the drug is independent from the dimension of the units of measurement and the same permeability values (in cm/sec) is obtained whether using molar concentration (M) or counts per minute (cpm) units. The permeability results for each compound were then compared to the octanol-water partition coefficient (XLogP) [57] reported for the specific substance (PubChem) to establish a comparative relation between permeability and lipophilicity. "
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    ABSTRACT: Background The study of the cerebrovascular physiology is crucial to understand the pathogenesis of neurological disease and the pharmacokinetic of drugs. Appropriate models in vitro often fail to represent in vivo physiology. To address these issues we propose the use of a novel artificial vascular system that closely mimics capillary and venous segments of human cerebrovasculature while also allowing for an extensive control of the experimental variables and their manipulation. Results Using hollow fiber technology, we modified an existing dynamic artificial model of the blood–brain barrier (BBB) (DIV-capillary) to encompass the distal post-capillary (DIV-venules) segments of the brain circulatory system. This artificial brain vascular system is comprised of a BBB module serially connected to a venule segment. A pump generates a pulsatile flow with arterial pressure feeding the system. The perfusate of the capillary module achieves levels of shear stress, pressure, and flow rate comparable to what observed in situ. Endothelial cell exposure to flow and abluminal astrocytic stimuli allowed for the formation of a highly selective capillary BBB with a trans-endothelial electrical resistance (TEER; >700 ohm cm2) and sucrose permeability (< 1X10-u cm/sec) comparable to in vivo. The venule module, which attempted to reproduce features of the hemodynamic microenvironment of venules, was perfused by media resulting in shear stress and intraluminal pressure levels lower than those found in capillaries. Because of altered cellular and hemodynamic factors, venule segments present a less stringent vascular bed (TEER <250 Ohm cm2; Psucrose > 1X10-4 cm/sec) than that of the BBB. Abluminal human brain vascular smooth muscle cells were used to reproduce the venular abluminal cell composition. Conclusion The unique characteristics afforded by the DIV-BBB in combination with a venule segment will realistically expand our ability to dissect and study the physiological and functional behavior of distinct segments of the human cerebrovascular network.
    BMC Neuroscience 02/2013; 14(1):18. DOI:10.1186/1471-2202-14-18 · 2.67 Impact Factor
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