Solubility predicted

Recently, several QSPR solubility prediction models based on a fairly large and diverse data set were generated. Huuskonen developed the models using MLRA and back-propagation neural networks (BPG) on a data set of 1297 diverse compoimds [22]. The compounds were described by 24 atom-type E-state indices and six other topological indices. For the 413 compoimds in the test set, MLRA gave = 0.88 and s = 0.71 and neural network provided... 

Tutorial Developing Models for Solubility Prediction with 18 Topological Descriptors... 

In order to develop a proper QSPR model for solubility prediction, the first task is to select appropriate input deseriptors that are highly correlated with solubility. Clearly, many factors influence solubility - to name but a few, the si2e of a molecule, the polarity of the molecule, and the ability of molecules to participate in hydrogen honding. For a large diverse data set, some indicators for describing the differences in the molecules are also important. 

QSAJi Methods for Fluid Solubility Prediction. Several group contribution methods for predicting Hquid solubiHties have been developed. These methods as weU as other similar methods are often called quantitative stmcture-activity relationships (QSARs). This field is experiencing rapid development. 

Delaney [4,14] and Klamt [16] argued that for drug-like compound datasets only about 20% of the variance of log S arises from AG s. This is further confirmed by the study of Wassvik et al. [15] in which 77% of the variance is due to the solubility of the supercooled liquid. Hence, applying crude estimates by mean values or by QSAR approaches we can reasonably expect that the inaccuracies introduced in dmg solubility prediction by our theoretical ignorance of AG s is less than, or at least not much bigger than, the inaccuracies introduced by the estimates of the larger park i.e. the liquid solubility, and by the experimental difficulties in solubility measurement. 

Fig. n.2 Schematic illustration of the states and modes of transfer relevant for solubility prediction. 

I J 7 Challenge of Drug SolubilitY Prediction and as H-bond interactions ... 

Ktihne, R., Ebert, R-U., Schtitirmann, G. Model selection based on structural similarity - method description and application to water solubility prediction. f Chem. Inf Model. 2006, 46, 636-641. 

It is advantageous with a new drug substance to be able to estimate what its solubility will be prior to carrying out dissolution experiments. There are several systems of solubility prediction, most notably those published by Amidon and Yalkowsky [14-16] in the 1970s. Their equation for solubility of p-aminobenzo-ates in polar and mixed solvents is a simplified two-dimensional analog of the Scatchard-Hildebrand equation and is based on the product of the interfacial tension and the molecular surface area of the hydrocarbon portion of a molecule. 

As a key first step towards oral absorption, considerable effort has been directed towards the development of computational solubility prediction [26-30]. However, partly due to a lack of large experimental datasets measured under identical conditions, today s methods are not sufficiently robust for reliable predictions [31]. Nonetheless, further fine-tuning of these models can be expected since high-throughput data have become available for their construction. 

Huuskonen, J., Salo, M., Taskinen, J., Aqueous solubility prediction of drugs based on molecular topology and neural network modeling, J. 

If we look at the physico-chemical factors governing solubility, among the first identified were log P [4] and melting point [5]. It can also theoretically be shown that these two factors describe solubility [6]. However, both these properties cannot be computed directly as molecular descriptors. It has been shown that solubility can be described more directly by molecular size, polarity and hydrogen bonding [7]. There are numerous studies on solubility predictions from directly computed descriptors (see Refs. [8-11]). 

Ruelle, P., Kesselring, U.W. (1997) Aqueous solubility prediction of environmentally important chemicals from the mobile order thermodynamics. Chemosphere 34, 273-298. 

It the productivity target cannot be achieved then a co-solvent system could be selected using solubility prediction methods like NRTL-SAC [1] and Local UNIFAC [4], The addition of a second solvent to increase solubility is an effective way of increasing productivity for a sparingly soluble solute. 

Solubility predictions in the Ml Aspen NRTL-SAC database at 25oC are presented in Table 8. 

We have studied the performance of several prediction methods to see how well in-house thermodynamic solubility measurements could be predicted. Among the prediction methods we studied were Huuskonen s method [26], ACD/Solubility DB [38], Meylan s method [21] as implemented in QMPRPlus, and the SimulationsPlus solubility prediction as implemented in QMPRPlus [39]. In general, we foimd the predictions to... 

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