The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.

We present an automated QSAR procedure that is used in AstraZeneca's AutoQSAR system. The approach involves automatically selecting the most predictive models from pools of both global and local models. The effectiveness of this QSAR modelling strategy is demonstrated with a retrospective study that uses a diverse selection of 9 early stage AstraZeneca drug discovery projects and 3 physicochemical endpoints: LogD; solubility and human plasma protein binding. We show that the strategy makes a statistically significant improvement to the accuracy of predictions when compared to an updating global strategy, and that the systematic biases inherent in the global model predictions are almost completely removed. This improvement is attributed to the model selection aspect of the strategy.

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Enzyme inhibiting activity X 0220Novel, Potent and Selective Anilinoquinazoline and Anilinopyrimidine Inhibitors of p38 MAP Kinase. -Via screening of a library of special quinazolines the initial hit is further modified leading to the discovery of two novel series of potent p38 inhibitors such as (I) and (II). -(CUMMING*, J. G.; MCKENZIE, C. L.; BOWDEN, S. G.; CAMPBELL, D.; MASTERS, D. J.; BREED, J.; JEWSBURY, P. J.; Bioorg. Med.

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