Molecular lipophilicity potential descriptor

Key Words 3D-QSAR hydrophobicity lipophilicity 3D-LogP conformation-dependent lipophilicity alignment-independent 3D descriptor molecular lipophilicity potential (MLP) ADME-related descriptor. 

The derivation of the 3D-LogP descriptor requires a suitable 3D space for the calculation of the molecular lipophilicity potential and validated MLP parameters. 

In addition to the steric and electrostatic descriptors, it was proposed to use other 3D molecular fields characterized by the sampling over the rectangular grid - in particular, the hydrophobic field/molecular lipophilic potential (MLP), ° hydrogen bonding and quantum-chemical parameters, e.g., orbital densities.Descriptor selection techniques are often recommended to enhance the stability, predictivity and interpretability of the CoMFA models. ... 

Figure 5.6 Complementarity between the major MFTA descriptor contributions to activity of the TIBO inhibitors of HIV-1 reverse transcriptase and the molecular properties of the biotarget protein a) atomic charge (Q) and electrostatic potential (EP) (b) atomic van der Waals radius (i ) and molecular surface (c) local lipophilicity (Lg) and molecular lipophilic potential (MLP) - see text for details.

Often ignored is the fact that log P/D is a conformation-dependent property [161], which has elegantly been demonstrated with the molecular lipophilicity potential (MLP) descriptor [87]. The MLP algorithm allows to calculate virtual log P values in conformational space. 

The relationship between the herbicidal activity of 1,2,5-oxadiazole iV-oxides and some physicochemical properties potentially related to this bioactivity, such as polarity, molecular volume, proton acceptor ability, lipophilicity, and reduction potential, were studied. The semi-empirical MO method AMI was used to calculate theoretical descriptors such as dipolar moment, molecular volume, Mulliken s charge, and the octanol/water partition coefficients (log Po/w) <2005MOL1197>. 

Plasma area under the concentration—time curves (AUCs) of 57 NCEs were determined following oral cassette administration (5—9 NCEs/cassette) to mice. Physicochemical properties [such as, molecular weight, calculated molar refractivity, and calculated lipophilicity (clogP)] and molecular descriptors [such as presence or absence of N-methylation, cyclobutyl moiety, or heteroatoms (non-C,H,0,N)] were calculated or estimated for these compounds. This structural data, along with the corresponding pharmacokinetic parameters (primarily AUC), were used to develop artificial neural network models [8]. These models were used to predict the AUCs of compounds under synthesis [10]. This approach demonstrates that predictive models could be developed which potentially predict in vivo pharmacokinetics of NCEs under synthesis. Similar examples have been reported elsewhere [11—13]. 

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