Drug design is an iterative process which begins with a compound that displays an interesting biological profile and ends with optimizing both the activity profile for the molecule and its chemical synthesis1. The process is initiated when the chemist conceives a hypothesis which relates the chemical features of the molecule (or series of molecules) to the biological activity. Without a detailed understanding of the biochemical process(es) responsible for activity, the hypothesis generally is refined by examining structural similarities and differences for active and inactive molecules. Compounds are selected for synthesis which maximizes the presence of functional groups or features believed to be responsible for activity1.
The combinatorial possibilities of this strategy for even simple systems can be explosive. As an example, the number of compounds required for synthesis in order to place 10 substituents on the four open positions of an asymmetrically disubstituted benzene ring system is approximately 10,0001.
The alternative to this labor intensive approach to compound optimization is to develop a theory that quantitatively relates variations in biological activity to changes in molecular descriptors which can easily be obtained for each compound2.
A Quantitative Structure Activity Relationship (QSAR) can then be utilized to help guide chemical synthesis. This chapter develops the concepts used to derive a QSAR and reviews the application of these techniques to medicinal research.
The physicochemical approach to drug design and discovery is to correlate mathematically chemical structure with biological activity induced by sets of congeneric drugs is now generally referred to as QSAR (quantitative structure-activity relationships).
QSAR is being used to gain insight into the interaction of drugs with macromolecules and macromolecular systems.
"A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics"
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.3
The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 104 .3
Solution: synthesize a small number of compounds and from their data derive rules to predict the biological activity of other compounds.
The combinatorial possibilities of this strategy for even simple systems can be explosive. As an example, the number of compounds required for synthesis in order to place 10 substituents on the four open positions of an asymmetrically disubstituted benzene ring system is approximately 10,0001.
The alternative to this labor intensive approach to compound optimization is to develop a theory that quantitatively relates variations in biological activity to changes in molecular descriptors which can easily be obtained for each compound2.
A Quantitative Structure Activity Relationship (QSAR) can then be utilized to help guide chemical synthesis. This chapter develops the concepts used to derive a QSAR and reviews the application of these techniques to medicinal research.
The physicochemical approach to drug design and discovery is to correlate mathematically chemical structure with biological activity induced by sets of congeneric drugs is now generally referred to as QSAR (quantitative structure-activity relationships).
QSAR is being used to gain insight into the interaction of drugs with macromolecules and macromolecular systems.
"A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics"
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.3
The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 104 .3
Solution: synthesize a small number of compounds and from their data derive rules to predict the biological activity of other compounds.
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