I-572 Summary
We have spent the semester discussing molecular modeling and
computational chemistry primarily from the viewpoint of chemistry and physics.
There are a large number of other topics that are more closely related to
chemoinformatics that we did not cover primarily because there are other courses
and means for you to learn these topics. In the working world this separation is not
reasonable and you will often find yourselves using whatever technique gives you a
good answer in the allotted amount of time.
Drug Discovery
An area that mixes computational chemistry and chemoinformatics. A “drug”
is often a small molecule that produces an effect by interacting with a biological
macromolecule. Often drugs are found by accident, sometimes by screening, by
rational design and many other methods. The overall process is expensive, fraught
with failure and necessary. In the best of situations you have a lead molecule that
shows some activity and a set of other molecules that have less or no activity so that
variations in structure and functional groups can be probed.
If we consider the overall path a drug takes in a human we can see the
various options for computational chemistry and chemoinformatics.
Oral drug
Mouth
Aqueous solubility, stable solid
Gut
Stable at pH < 1, soluble in aqueous acid
Intestine
Lipid soluble, able to pass through membranes
Blood stream
Soluble at pH ~ 7
Cell
pass through cell membrane, recognizes
Macromolecule, binding
Need: ADME - water solubility, logP, Toxicity, acid stability
Binding – docking, conformational searching,
There is a wide range of information required in the drug discovery process and not
all of it is experimentally available. Some properties can be computed using
computational chemistry methods – docking, qm and mm estimation of charges,
conformational searching, MC for logP. But many other properties are predicted
using informatics/engineering methods based on the analysis of available data –
logP, solubility, melting and boiling points, vapor pressure and molar refractivity.
These methods may be group based or atom based but they are interpolation
methods and only as good as the experimental data base. The purpose of all these
methods is too rapidly find new potential drugs and eliminate those molecules
which are not likely to be useful.
Property Estimation References
M. Clark, “Generalized Fragment-Structure Based Property Prediction Methods”,
J.Chem.Inf.Model, 2005, 45, 30-38.
S.E. Stein and R.L. Brown, “Estimation of Normal Boiling Points from Group
Contributions”, J.Chem.Inf.Model, 1994 34, 581-587.
R. Wang, Y. Fu, and Luhua Li, “A New Atom-Additive Method for Calculating
Partition Coefficients”, J.Chem.Inf.Model, 1997 37, 615-621
T.J. Hou, K. Xia, W. Zhang, and X.J. Xu, “ADME Evaluation in Drug Discovery. 4.
Prediction of Aqueous Solubility Based on Atom Contribution Approach”,
J.Chem.Inf.Model, 2004 44, 266-275.
S. Wildman and G. Grippen, “Prediction of Physiochemical Parameters by Atomic
Contributions”, J.Chem.Inf.Model, 1999 39, 868-873.
N. Jain and S. Yalkowsky, “Estimation of Aqueous Solublitiy”, J. Pharm. Sci, 90,2001,
234-252.
L. Constantinou and R. Gani, “New Group Contribution Method for Estimating
Properties of Pure Compounds”, AICHe Journal, 1994, 40, 1697-1710.
G. Klopman, J. Li, S. Wang and M. Dimayuga, “Computer Automated logP Calculations
Based on Extended Group Contribution Approach”, J.Chem.Inf.Model, 1994 34, 752781.
Given a set of active molecules one can construct a 3-D pharmacophore, a set
of features common to a series of active molecules arranged in 3D space. The groups
normally included would be the standard reactive functional groups – hydrogen
bond donors and acceptors, negatively and positively charged groups and
hydrophobic regions. These are the groups most likely to interact with the binding
site and their arrangement in 3D space usually determines the activity of a
particular molecule. Once a pharmacphore is defined it is possible to search a 3D
database of molecules looking for new structures that contain the pharmacophore.
There are two problems to consider when calculating 3D pharmacophores. First ,
unless the molecules are all completely rigid, one must take account of their
conformational flexibility and the second is to determine which set of
pharmacophores is common to all the molecules. It is important to remember that
all approaches to finding 3D pharmacophores assumes the molecules all bind in the
same manner.

One of the most used methods for finding 3D pharmacophores is the
constrained systematic search. Starting from the most conformationally restricted
molecule you find the lowest energy conformations and identify the positions of the
pharmacophore groups. These positions provide restraints which can be used in the
conformational searches on the more flexible ligands, eliminating entire regions of
conformational space. Only those torsion angles that allow the pharmacophore
groups to occupy the same positions as in the first molecule need be checked. As the
search on additional ligands proceeds the search space becomes more restricted.
Once a 3D pharmacophore has been determined the types of functional
groups and the distances between them can be used to search a 3D database of
structures for similar types of molecules that satisfy the 3D pharmacophore
requirements. The ligands found in the database search can then be analyzed using
the various chemoinformatics methods to develop a set of descriptors and these can
then be used to predict the various ADME properties and eliminate those ligands
that would clearly fail.
Finally, given a set of molecules, a set of descriptors and biological activities
it may be possible to do a CoMFA analysis. The presumed active conformations of
each ligand must be overlaid in the proposed binding site and then the molecular
fields surrounding each molecule can then be calculated by placing appropriate
probe groups at points on a regular lattice that encompasses the molecule. (Similar
to the Grid scoring in Dock). A matrix of grid values (energies) and activities is
generated and a correlation between the biological activity and the field values
(grid values) is then determined:
N
P
activity  C    c ij Sij
i1 j1
where N is the number of grid points, P is the number of probe groups (+,-,vdw). A
partial least squares analysis is done and a coefficient for each column in the table is
generated. The value indicates the significance of each grid point in explaining the
activity. Such data can then be plotted as 3D contour plots and can be used to
identify regions where changes in structure would increase or decrease binding.
CoMFA can be particularly useful in the design of compounds that are selective for
one target over another, but it has real problems when the binding site or the
binding conformation is poorly defined. CoMFA is not the only 3D qsar method
available.
Drug design is a large field and this has been a very simplified introduction
to the topic. I would strongly suggest you obtain a book such as the one by Leach,
“Molecular Modeling” and keep it as a reference and starting point for your work in
these areas.