**
Octanol/water partition coefficient, log P
**

There are two **log P** models in ADMET Predictor™: S+logP and MlogP. The S+logP model is based on artificial neural network ensembles (ANNE) constructed by our automatic model builder ADMET Modeler™ from almost 13,000 example compounds selected from the "StarList" of ion-corrected experimental logP values (Hansch, C. et al, 1995). The MlogP model, which is based on the published work of Moriguchi, et al (Moriguchi et al; 1992 and 1994), was the sole log P predictor in very early versions of ADMET Predictor and continues to be retained for comparison.

The figure below shows the correlation between the observed logP and the predicted S+logP values.

*ADMET Predictor 2D S+logP Model Validation*

In all of the independent model comparisons performed on public as well as industrial data sets, S+logP has consistently outperformed other predictive logP models available up to date, see (Dearden et al; 2003), (Tetko, et al; 2007) and (Mannhold, et al; 2008) for details.

**
Octanol/water distribution coefficient, log D
**

The octanol-water distribution coeffcient, **log D**, is determined by the ratio of *analytical* concentrations in octanol and water phases, respectively (Pagliara; 1997). As such, it includes all protonation states of a given compound and does depend on pH. Consequently, it is much more difficult to model log D, than log P. Its complete pH profile can be derived from pH-dependent distribution of protonation species in both solvents, as expressed by the equation below:

where the pH dependence of the model is contained in the pH-dependent fractions ionized, f^{(0)} and f^{(i)}, which can be obtained from knowledge of the aqueous pKa's of the molecule. The sum goes over all ionized states of the molecule. The d^{(i)} factors contain the differences between log P of the neutral species and log P(i), the log P of a given ionization state. Often, the approximation is made that the d^{(i)} are the same for all anionic states of a molecule and can be set to a single *constant*, structure-independent, value d^{(-)}. Similarly, all cationic states are set to a single *constant* value d^{(+)}. Such assumptions were made in earlier versions of our S+logD model. The present S+logD model is quite unique in the sense that all the d^{(i)} factors are now structure-dependent and calculated by separate artificial neural network ensembles (ANNE) from molecular descriptors. Our pKa model delivers pKa values necessary to calculate fractions ionized and S+logP provides the log P values. Thus, the structure-dependent S+logD model vastly outperforms our earlier version with constant d^{(i)} factors. It has been trained on almost 7,600 non-ion-corrected log D values obtained from BioByte's Masterfile as well as additional measurements extracted from the literature.

*ADMET Predictor 2D S+logD Model Validation*

**
Air/water partition coefficient, log HLC
**

Henry’s Law Constant (HLC) is a key physical property that represents the air-water equilibrium partition coefficient for a chemical compound present in a dilute aqueous solution and reflects the relative volatility of the compound. The Simulations Plus' S+logHLC model predicts Henry’s Law Constant in the units of atm.m^{2}/mol at 25 °C (298.15 K). The model is developed with Artificial Neural Network Ensemble (ANNE) methodology for a dataset of 530 compounds curated from U.S. Environmental Protection Agency (EPA) web site.

*ADMET Predictor S+logHLC Model Validation*

Hansch, C. et al, "Exploring QSAR: Hydrophobic, Electronic, and Steric Constraints." ACS Publications (1995).

Moriguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y. "Simple method of calculating Octanol/Water Partition Coefficient." Chem Pharm Bull. 1992; 40:127-130.

Dearden JC, Netzeva TI, Bibby R. "A Comparison of Commercially Available Software for the Prediction of Partition Coefficient." in "EuroQSAR 2002: Designing Drugs and Crop Protectants: Processes, Problems and Solutions", Bournemouth, UK, Blackwell Publishing Ltd. (2003)

Tetko IV and Poda GI. "Prediction of Log P with Property-Based Methods." in "Molecular Drug Properties: Measurement and Prediction." ed. Mannhold R. Weinheim, Germany, Wiley-VCH: 381-406. (2007)

Mannhold R, Poda GI, Ostermann C, Tetko IV. "Calculation of Molecular Lipophilicity: State of the Art and Comparison of Methods." J Pharm Sci. 2008; in press.

Pagliara A, Carrupt P-A, Caron G, Gaillard P, Testa B. "Lipophilicity Profiles of Ampholytes." Chem Rev. 1997; 97:3385-3340.