Lead optimization quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) models have proven their utility, from both the pharmaceutical and toxicological perspectives, for the identification of chemicals that might interact with ER. While their primary function in the pharmaceutical enterprise is lead discovery and optimization for high-affinity ER ligands, QSAR models can play an essential role in toxicology as a priority-setting tool for risk assessment. 

Han van de Waterbeemd, Bernard Testa, and Gerd Folkers, Computer-Assisted Lead Finding and Optimization. Current Tools for Medicinal Chemistry, in European Symposium on Quantitative Structure-Activity Relationships, Vol. 11, Wiley-VCH, Weinheim, 1997. 

Cruciani G, Crivori P, Carrupt PA, Testa B (2000) Molecular fields in quantitative structure-permeation relationships The VolSurf approach. Theochem 503 17-30 Cruciani G, Pastor M, Clementi S (2000) Handling information from 3D GRID maps for QSAR studies. In Gun-dertofte K, Jorgensen FS (eds) Molecular modelling and prediction of bioactivity, proceedings of the 12th European symposium on quantitative structure-activity relationships (QSAR 98). Plenum Press, New York, pp 73-81 Cruciani G, Pastor M, Guba W (2000) VolSurf A new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sd 11 S29-S39... 

In addition, substituent properties can be used systematically to find quantitative structure-activity relationships (QSAR) during lead optimization in structures of pharmaceutical interest [10, 11]. 

Another field for assisting in the selection of potential lead compounds or optimized leads is called Quantitative Structure Activity Relationships (QSARs). These methods determine correlations between certain descriptors of the molecular structures and the measured biological activity to produce a predictive model. That model can then be used to predict the activity of other structures prior to actually testing. 

Quantitative structure-activity relationships QSAR. The QSAR approach pioneered by Hansch and co-workers relates biological data of congeneric structures to physical properties such as hydrophobicity, electronic, and steric effects using linear regression techniques to estimate the relative importance of each of those effects contributing to the biological effect. The molecular descriptors used can be 1-D or 3-D (3D-QSAR). A statistically sound QSAR regression equation can be used for lead optimization. 

AP, that result from the application of particular transformations, AS, and hence provide an inverse quantitative structure-activity relationship (QSAR) approach to lead optimization [19]. Another difference is that, given an appropriate source of data, they can model not only biological activity, the principal focus of the bioisosterism approaches, but also any chemical, physicochemical, or ADMETproperty that needs to be optimized. 

Thus, the discipline of medicinal chemistry is actually a collection of different scientific disciplines, lead optimization being just one of them. Worse, the theoretical aspects of optimization are routinely conflated with, and subjugated to, the synthetic aspects of medicinal chemistry because recruitment of medicinal chemists is almost exclusively from the ranks of synthetic organic chemistry schools. Therefore, unlike fields such as engineering, it is uncommon to see discussions on lead optimization as a separate science. Corwin Hansch, one of the founders of a scientific discipline that is very relevant to this discussion, quantitative structure-activity relationships (QSARs), wrote an exasperated final paragraph to his definitive textbook ... 

It is a fact that "serendipity" still dominates the discovery of an entirely new class of pesticides, especially one with novel mode of action. The reason is simply because our ability to design compounds to affect enzymes, receptors, or other biochemical targets is still very limited. Modem molecular modeling techniques and the computer-aided study of quantitative structure activity relationship are mainly for the optimization of an original lead (A). 

H. Kubinyi, in Computer-Assisted Lead Finding and Optimization Proc. 11th European Symp. on Quantitative Structure-Activity Relationships, Lausanne, 1996, eds. van de H. Waterbeemd, B. Testa, and G. Folkers, Verlag Helvetica Chimica Acta and VCH Basel, Weinheim, 1997, pp. 7-28. 

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