Molecular recognition refers to the specific interactions between two or more molecules through noncovalent bonding such as hydrogen bonding, metal coordination, hydrophobic forces, van der Waals forces, etc. Here we will be taking about molecular recognition in terms of drug design. Molecular recognition for drug design involves target selection, lead discovery and lead development.

Target selection involves the site or molecule we would like to modify.

A drug is a small molecule (ligand) able to bind a therapeutic target (enzyme, receptor,etc.) and modulate its activity.

The most important molecules in drug design are enzymes. The basic mechanism by which enzymes catalyze chemical reactions begins with the binding of the substrate (or substrates) to the active site on the enzyme. The active site is the specific region of the enzyme which combines with the substrate. The binding of the substrate to the enzyme causes changes in the distribution of electrons in the chemical bonds of the substrate and ultimately causes the reactions that lead to the formation of products. The products are released from the enzyme surface to regenerate the enzyme for another reaction cycle. [1]

The active site has a unique geometric shape that is complementary to the geometric shape of a substrate molecule, similar to the fit of puzzle pieces. This means that enzymes specifically react with only one or a very few similar compounds.

Lock and Key Model

The specific action of an enzyme with a single substrate can be explained using a Lock and Key analogy first postulated in 1894 by Emil Fischer. In this analogy, the lock is the enzyme and the key is the substrate. Only the correctly sized key (substrate) fits into the key hole (active site) of the lock (enzyme).

Induced Fit Model

Not all experimental evidence can be adequately explained by using the so-called rigid enzyme model assumed by the lock and key theory. The induced-fit theory assumes that the substrate plays a role in determining the final shape of the enzyme and that the enzyme is partially flexible. This explains why certain compounds can bind to the enzyme but do not react because the enzyme has been distorted too much. Other molecules may be too small to induce the proper alignment and therefore cannot react. Only the proper substrate is capable of inducing the proper alignment of the active site.

Molecular recognition is the collection of interactions between molecules that allow their binding. This depends on the nature of interactions and the intensity of molecular recognition. The interactions include electrostatic interactions, van der Waals interactions, hydrophobic interactions and other noncovalent interactions. Other factors such as the solvation effect, π interactions, intramolecular changes upon binding, and entropy changes upon binding also effect in determining the recognition strength.

Theoretical approaches for estimating binding affinities

Without the 3D structure of the complex we can use:

• 2D - QSAR
• 3D - QSAR

With the 3D structure of the complex, we can use:

• Physical based scoring functions
• Empirical-based scoring functions
• Knowledge-based approaches

QSAR - Quantitative Structure Activity Relationships

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 synthesis. 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 maximize the presence of functional groups or features believed to be responsible for activity. [2]

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 10,000. 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 compound. A Quantitative Structure Activity Relationship (QSAR) can then be utilized to help guide chemical synthesis. [2]

QSAR assumes that chemically similar ligands produce biologically similar responses. It also assumes that affinity is a function of the ligand's physico-chemical properties. The advantage of this technique is that we do not need any structural information about the target. This technique, however, requires knowledge of affinities for a series of ligands adn knowledge of structurally related ligands or similar binding modes. A QSAR attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these "rules" can be used to evaluate new chemical entities.

We take n structurally related molecules and we make a table of their quantitative descriptions vs. measured activities. Descriptors can be volume, electrostatics, hydrophobicity, etc. We then use this to calculate δG for binding. This data is used as training set molecules.

2D QSAR is limited to structurally related molecules. It requires experimental activity of a series of ligands, not ab initio studies. This method suffers from overfitting like many training algorithm based methods. The use of a particular QSAR is limited to the descriptors used in the training set.

3D QSAR is the same as 2D QSAR with the addition of x,y,z coordinates of the atoms. It also needs experimental activity of a series of ligands. It is NOT limited to structurally related molecules.

Free Energy Simulation

Sources

[1] http://www.elmhurst.edu/~chm/vchembook/571lockkey.html
[2] http://www.netsci.org/Science/Compchem/feature19.html