Modern 2D QSAR for drug discovery

2D QSAR is a powerful tool for explaining the relationships between chemical structure and experimental observations. Key elements of the method are the numerical descriptors used to translate a chemical structure into mathematical variables, the quality of the observed data and the statistical methods used to derive the relationships between the observations and the descriptors. There are some caveats to what is essentially a simple procedure: overfitting of the data, domain applicability to new structures and making good error estimates for each prediction. 2D QSAR models are used routinely during the process of optimization of a chemical series towards a candidate for clinical trials. As more knowledge is gained in this area, 2D QSARs will become acceptable surrogates for experimental observations. WIREs Comput Mol Sci 2014, 4:505–522. doi: 10.1002/wcms.1187

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