automated QSAR

The main principles of the drug discovery process rely on a correlation existing between a chemical structure and it’s activity. This #BucketListPapers integrates two methods of finding these. Several different promising methods have arose that attempt to find the relationship between structure and biological activity. These are quantitative structure-activity relationship (QSAR), pattern recognition methods (PR) and discriminant analysis (DA). Klopmann introduced a new method that combines the latter two. The method fragments a molecule into varying size fragments, from 3 to 12 interconnecting atoms. The hydrogens are retained along with information about the multiplicity of the bonds and whether an atom is connected to a terminal functionality group. Statistical analysis is then performed on each of the fragments to indicate whether it is an important feature in an active or inactive molecule, where the active fragments are known as biophores and the inactive fragments are biophobes.

Three studies were carried out in order to understand how useful this method was. Two of the studies already had existing information and the biophores and biophobes that were extracted were reasonable and aligned with previous studies. Additionally, the models correctly identifying 35/43 and 41/43 respectively. The image below demonstrates three fragments, two activating and on deactivating. The two activating fragments show a bay region that had previously, in a different study, been considered to be important in the binding, which the deactivating fragment does not possess. The last study there was no known information about what caused molecules to be active or inactive. There were 10 identified statistically relevant biophores/ biophobes. When using these biophores and biophobes the model was able to correctly predict 33/39.

Klopmann, therefore, showed that this approach had the potential to automatically extract information and turn them into useful biophores and biophobes. This method paved the way in applying artificial intelligence approaches to structure-activity studies.

Klopmann: Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules
J. Am. Chem. Soc. 1984, 106, 24, 7315–7321

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