Computational methods high throughput measurements

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. 

Handling such interwoven networks and complex feedback loops is beyond the capability of common laboratory methods, not to mention that just the complexity of scientific literature itself is already beyond measure. Help from computers and bioinformatics has become a must in today s biomedical research. In fact, bioinformatics methods have become indispensable for each step in biomedical research, from high-throughput data collection to clinical decision support. This chapter focuses on the application of bioinformatics methods in the study of pharmacogenomics, drug discovery, and systems biology. 

The aqueous solubility of a compound is another key property in drug discovery and poor solubility is often the cause of a series demise. Aqueous solubility is inversely related to lipophilicity. High-throughput methods are now available to measure solubility, " but approaches to estimate solubility from computational models are less suc-cessfnl. The models are not sufficiently accurate to predict poor solubility with great precision. Moreover, solubility is influenced by many factors such as ionic strength, type of buffer, crystal packing, etc., all properties which are difficult... 

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