Chapter 10: Application of Modelling Techniques Check Access

QSAR is defined by its embrace of quantitative modelling to build relationships between a molecule's structure and its properties. It was brought into being by Corwin Hansch's application of linear regression to biological activity data.

Restricted to substituent constants extracted from experiments and hand-crafted into tables, graphs and simple regression models, early QSAR was very limited in scope but had the advantage that the practitioner became intimately connected to the data and the patterns that emerged from an analysis could be fitted into a coherent chemical explanation.

There has been a profound shift in the capabilities available to us as QSAR modellers, particularly in the expansion of readily calculated chemical descriptors and model building methods, but at the heart of it remains the question of whether we are seeking models that the human expert can understand and interpret, or models that are useful, validated and predictive?

Thischapter describes a framework for understanding the model building options available in the context of drug discovery decision making and points to more recent work that seeks to identify more rigorous quantifiable metrics for assessing performance of data mining techniques in QSAR modelling. The chapter highlights common modelling methods used in QSAR, with examples, and also covers more recent ensemble and meta-modelling developments.