ALOGP

K, V N Viswanadhan and J J Wendoloski 1998. Prediction of Hydrophobic (Lipophilic) lerties of Small Organic Molecules Using Fragmental Methods An Analysis of ALOGP and GP Methods. Journal of Physical Chemistry 102 3762-3772. 

Caron, G Ermondi, G. Calculating virmal log P in the alkane/water system (logPait) and its derived parameters AlogP/i ait and logDJ. /. Med. Chem. 2005, 48, 3269-3279. 

Chose, A. K., Viswanadhan, V. N., Wendoloski, 1.1. Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods an analysis of ALOGP and CLOGP Methods./. Phys. Chem. 1998, A 102, 3752-3772. 

ALOGPS Virtual Computational Chemistry Laboratory, Germany http //WWW. vcclab.org 2D 79, 87, 98... 

In other studies a 2- to 10-fold increase in the predichon accuracy of ALOGPS was demonstrated following extension of the training set in the LIBRARY mode [80-83]. 

Other difficulties arise from the incorrect treatment of aromaticity, e.g. the tautomeric form C can be also represented as an aromatic compound. However, ALOGPS, for example, does not consider this ring as an aromatic one. Inconsistently defined aromaticity lowers the prediction performance (Fig. 15.ID). The use of SDF files, which do not explicitly define aromaticity solves this problem. All these factors are not limiting when the data are prepared with the same coding scheme and consistency. However, these issues are very important for method application and benchmarking. 

Tetko, I. V., Tandiuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. 

Tetko, I. V., Bruneau, P. Application of ALOGPS to predict 1-octanol/water distribution coefficients, log P, and log D, of AstraZeneca in-house database. 

However, there is still a strong need to develop new methods that will be able to quantitatively or at least qualitatively estimate the prediction accuracy of log D models. Such models will allow the computational chemist to distinguish reliable versus nonreliable predictions and to decide whether the available model is sufficiently accurate or whether experimental measurements should be provided. For example, when applying ALOGPS in the LIB RARY model it was possible to predict more than 50% and 30% compounds with an accuracy of MAE <0.35 for Pfizer and AstraZeneca collections, respectively [117]. This precision approximately corresponds to the experimental accuracy, s=0.4, of potentiometric lipophilicity determinations [15], Thus, depending on the required precision, one could skip experimental measurements for some of the accurately predicted compounds. 

Tetko, I. V., Poda, G. I. Application of ALOGPS 2.1 to predict logD distribution coefficient for Pfizer proprietary compounds. J. Med. Chem. 2004, 47, 5601-5604. 

Seiler [250] proposed a way of estimating the extent of hydrogen bonding in solute partitioning between water and a lipid phase by measuring the so-called A log P parameter. The latter parameter is usually defined as the difference between the partition coefficient of a solute measured in the octanol-water system and that measured in an inert alkane-water suspension AlogP = log Kp oet — log Kp aik. 

T. J., Azole endothelin antagonists. 3. Using AlogP as a tool to improve absorption, J. Med Chem. 39, 982-991 (1996). 

Chait, A., Zasiavsky, B., ALOGP -A new automated log P direct partitioning workstation, LogP2000, Lausanne, 2000, Switzerland. 

Nevertheless, Absolv gave us five additional parameters to work with. What is more, we used ALOGP, the predicted octanol/water partition coefficient also calculated by Absolv. We combined these descriptors with CLOGP, CMR and those from HYBOT. Forward step-wise regression gave Eq. (7) ... 

ALOGP Logarithm of the calculated octanol/water partition coefficient c Concentration... 

Compound Logk (MEEKC) LogP (MEEKC) LogP (literature) ALogP pKa (CE)... 

---