We have already discussed ways of representing and characterizing 3D structures, including conformer generation, energy minimization and 3D pharmacophore and similarity searching. In this section, we will look molecular visualization, and then how 3D structures can be used in a variety of computational fashions either separately from or in conjunction with a protein target structure.

Visualization of 3D structures and proteins

The simplest and most common way to visualize a 3D structure is with the CPK ball and stick model:


Interestingly, molecular visualization was introduced very early (1960's and 70's) and until high quality graphics became commoditized in the 1990's, was one of the main applications of high-end graphics workstations. As a comparison, here are a molecular image taken from an Evans & Sutherland workstation running Sybyl, and the current version of Microsoft Windows, both taken in 1988 (Windows 2.0 image taken from www.guidebookgallery.org):


There are plenty of programs available for visualizing and rotating and translating molecules in this fashion (and with a variety of other bells and whistles). Most of them are free. Some work in web browsers; some are standalone; some produce images. Most work with proteins as well as small molecules. A few notable ones are

There are plenty of tutorials available, particularly using JMol. Here are some:

Some great work on advanced molecular visualization has been done at the Scripps Molecular Graphics Laboratory .

Molecular Superposition

Molecular superposition involves aligning two or more molecules in 3D (either to each other or to a single rigid reference molecule) so they optimally overlay in some fashion. Alignment is done by rotating and translating the molecules, and by flexing and rotating bonds to create different conformers. Superimposition can be a prerequisite to 3D similarity searching, pharmacophore detection, and 3D QSAR.

Superposition is an example of optimization, and thus can use one of a variety of methods, from simple hill climbing to genetic algorithms, simulated annealing and monte-carlo. Example commercial packages are GASP and Accelrys Discovery Studio HipHop/Hypogen . Other methods have been used for alignment, for example shape-based overlay (see OpenEye ROCS ) or field-based overlay.


3D QSAR can be done simply by using 3D descriptors instead of 2D. However, there are a variety of other ways of doing QSAR in 3D. One well known one is CoMFA (Comparative Molecular Field Analysis). CoMFA uses fields to optimize overlay of multiple structures based on features related to binding (electrostatics, sterics, hydrophobics), finds commonality in the overlaid fields, then correlates these areas of commonality with activity. It requires that structures be already aligned, and can be used predictively or for visualization. For more information, see the CoMFA tutorial and the NetSci article . A nice example of its use with D2 agonists is in another NetSci article .

Molecular Docking

Molecular docking involves attempting to predict how compounds ("ligands") might bind to the active sites of protein targets (usually to predict inhibition activity). Most of them rotate and translate molecules in an active site, flexing rotatable bonds, until some function relating to binding is minimized. This scoring function often is made of some combination of hydrogen bonding, electrostatic interactions, shape, and hydrophobic interactions. The final value of the scoring function is generally taken as a measure of the success of the docking. Whilst docking algorithms have proven quite good at replicating binding orientations of compounds in active sites (testes using RMSD from bound ligands in crystal structures), the relationship of the final value of the scoring function to binding affinity is much less clear. Some of the more popular docking programs are:

Molecular Modeling Tools

There are a variety of molecular modeling tools available that can do superposition, 3D QSAR, docking, and/or quantum calculations. Here are a few:

AutoDock Tutorial