Lewis, Richard and Wood, Dave (2014) Modern 2D QSAR for Drug Discovery. computational molecular science, 4. pp. 505-522.

Abstract

2D QSAR is a powerful tool for explaining the relationships between chemical structure and experimental observations. Key to the method are the numerical descriptors used to translate a chemical structure into a mathematical variable, the quality of the observed data and the statistical methods used to derive the relationships. There are many caveats to what is essentially a simple procedure, not limited to 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 start to be acceptable surrogates for experimental observations.

Item Type:	Article
Date Deposited:	27 Apr 2016 23:45
Last Modified:	27 Apr 2016 23:45
URI:	https://oak.novartis.com/id/eprint/20553