The field of quantitative structure activity relationships (QSAR) has evolved into an integral tool for pharmaceutical discovery. It is presently an accessible technology, as can be shown by the number papers which are easily found through PubMed literature searches. At one level, QSAR is used routinely and invisibly as an aid for the bench chemist for logP, logS, pKa/pKb, metabolic stability and other such properties. Chemoinformaticians and computational chemists develop models from scratch for less routine purposes associated with lead optimization around a single target or library design around a target family such as kinase, ion channel or GPCR inhibitors. Regardless of the differences in frequency of use and the end user, any successful QSAR is successful because it rests on appropriate mathematics linking valid data and relevant descriptors. Though success is defined by the end user, the QSAR originator is well advised to validate his model and understand how it performs in different situations. The present review will cover QSAR from the ground up as it is used in pharmaceutical research environments. It will focus towards larger dataset methodologies (a minimum 100 of compounds) and by consequence will focus on 2D descriptors. It will start with the critical base of data, descriptors, equations and validation methods. It will review the broadly used and invisible QSARs for logP, pKa/pKb and metabolic stability. The review will then present progress in QSARs of broad interest which are under active development: 1) hERG liability models, 2) modeling for 2a) drug-likeness and related properties, 2b) kinase ligand likeness and 2c) GPCR ligand likeness.