Druglikeness prediction

Several different approaches have been used to generate druglikeness prediction tools. The simplest approach is to identify a range of descriptor values for druglike compounds. The best known example of this approach is the Lipinski rules [16], which predict poor... 

Molecular weight Predicted druglikeness Predicted solubility Predicted toxicity Selectivity... 

Poor pharmacokinetics and toxicity are important causes of costly late-stage failures in drug development. It is generally recognized that, in addition to optimized potency and specificity, chemical libraries should also possess favorable ADME/Tox and druglike properties [77-80]. Assessment of druglike character is an attempt to decipher molecular features that are likely to lead to a successful in vivo and, ultimately, clinical candidate [81-83]. Many of these properties can be predicted before molecules are synthesized, purchased, or even tested in order to improve overall lead and library quality. 

I. V. In silica approaches to prediction of aqueous and DMSO solubility of druglike compounds trends, problems and solutions. Curr. Med. Chem. 2006, 13, 223-241. 

S. O. Prediction of pH-dependent aqueous solubility of druglike molecules. J. Chem. Inf. Model. 2006, 46, 2601-2609. 

The differences can also be seen in Figure 15.4, which compares cumulative distributions of these properties between the Gasteiger and Roche datasets. Many of the literature compounds are very simple, with low molecular weight, few polar atoms, and few functional groups. They have often been included in solubility datasets because they are well characterized and because accurate solubility data are available for them, rather than because they are druglike. The inclusion of many such simple compounds in a training set for a solubility prediction tool may focus the tool on an area of chemistry space that is not well populated with druglike molecules and may make the tool less useful for the prediction of the solubility... 

A reliable druglikeness predictor would give high prediction scores only to compounds that have satisfactory properties based on aU of these criteria, or in other words, there would be few false positives. There has been considerable effort expended over the last 10 to 20 years in modeling individual components of this process, including solubility [18-36], ADME properties [53-70], and toxicities [71-88]. Individually, each of tliese predictions has false positives and false negatives, so it is difficult to expect... 

Most prediction tools for druglikeness use the MDL Available Chemicals Directory (ACD) [89] as a list of nondrugs and one of three databases as a list of drugs MDL Drug Data Report (MDDR) [89], MDL Comprehensive Medicinal Chemistry (CMC) [89], or Derwent World Drug Index (WDI) [90]. 

Fig. 15.6 The percentage of compounds predicted druglike as a function of molecular weight.

Fig. 15.15 S elected examples of the distribution of predicted druglikeness scores (DN D drug neural network) for a library of 2052 compounds. As described in Section 15.4, druglikeness scoring was performed with a neural network to predict druglikeness using the dataset kindly provided by Kubinyi and Sadowski. Scores close to 1.0 predict druglikeness scores approaching zero predict nondruglikeness.

The application of computational algorithms for compound filtering and clustering is routinely used to eliminate undesired structures on the basis of chemically reactive fimctionalities, predicted liabilities (e.g., frequent hitters, hERG, cyp450 and so forth), or druglike properties. Computational methods are also used to group compoimds on... 

Structure (518-522). In addition, the advent of combinatorial chemistry has focused modeling efforts on prioritizing compounds (523-528) for high throughput screening based on chemical diversity (529-531), druglike properties (532,533), predicted oral bioavailability (534,535), and so forth. 

Hansen, N.T., Kouskoumvekaki, I., Jorgensen, F., Steen, B., Soren, J. and Svava, O. (2006) Prediction of pH-dependent aqueous solubility of druglike molecules. Journal of Chemical Information and Modeling, 46 (6), 2601-2609. 

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