Use of Semiempirical Properties in SAR

This area is a development in the usage of NDDO models that emphasizes their utility for large-scale problems. Structure-activity relationships (SARs) are widely used in the pharmaceutical industry to understand how the various features of biologically active molecules 

if a drug company has a database of several hundred thousand molecules that it has Synthesized over the years, and it has measured molecular properties for those compounds, once it identifies a SAR for some particular bio-target, it can quickly run its database through the SAR to identify other molecules that should be examined. However, this process is not very useful for identifying new molecules that might be better than any presently existing ones. It can be quite expensive to synthesize new molecules randomly, so how can that process be similarly prioritized  

One particularly efficient alternative is to develop SARs not with experimental molecular properties, but with predicted ones. Thus, if the drug company database is augmented with predicted values, and a SAR on predicted values proves useful based on data for compounds already assayed, potential new compounds can be examined in a purely computational fashion to evaluate whether they should be priority targets for synthesis. In 1998, Beck et al. (1998) optimized the geometries of a database of 53000 compounds with AMI in 14 hours on a 128-processor Origin 2000 computer. Such speed is presently possible only for semiempirical levels of theory. Once the geometries and wave functions are in hand, it is straightforward (and typically much faster) to compute a very wide variety of molecular properties in order to survey possible SARs. Note that for the SAR to be useful, the absolute values of the computed properties do not necessarily need to be accurate - only their variation relative to their activity is important. 

This area is a development in the usage of NDDO models that emphasizes their utility for large-scale problems. Structure-activity relationships (SARs) are widely used in the pharmaceutical industry to understand how the various features of biologically active molecules contribute to their activity. SARs typically take the form of equations, often linear equations, that quantify activity as a function of variables associated with the molecules. The molecular variables could include, for instance, molecular weight, dipole moment, hydrophobic surface area, octanol-water partition coefficient, vapor pressure, various descriptors associated with molecular geometry, etc. For example, Cramer, Famini, and Lowrey (1993) found a strong correlation (r = 0.958) between various computed properties for 44 alkylammonium ions and their ability to act as acetylcholinesterase inhibitors according to the equation 

128-processor Origin 2000 computer. Such speed is presently possible only for semiempirical levels of theory. Once the geometries and wave functions are in hand, it is straightforward (and typically much faster) to compute a very wide variety of molecular properties in order to survey possible SARs. Note that for the SAR to be useful, the absolute values of the computed properties do not necessarily need to be accurate - only their variation relative to their activity is important. 

---