In typical QSAR, the properties of the target is neglected total from the anaysis. With the advent of the genomics revolutation, things have changed radically. One of the issues in drug design is that some targets undergoes mutational changes, whcih can be seen in the resistance in antibiotics and antivral compounds. During the treatment mutations may occur in the gene of the pathogen, which may induce its resistant to the tratment. Because of the evolutationary pressure that mutant forms that are most viable under the tratment. And also for drug resistnace during treatments of cancer. Development of drug resistnace is a problem in drug development, with the mutations constantly change the structural properties of the drug targets. The chemist is then forced to develop new drugs that interact efficiently with the new mutated targets.

Proteochemometrics (PCM) allows to to analyze virtually any number of targets and mutant variants simultaneously. It is a true merger of bioinformatics and cheminformatics as it can be viewed as an extension of QSAR, by utilising the chemical information with a series of chemical compounds and a series of biological targets. Just like QSAR, the compound series and the target series are then described by the using of chemical descriptors. To represent the compounds we will to use the substructure fingerprint count because it is highly interpretable and very fast when generating descriptors. For the protein, typically z-scaled descriptors were used to represent the protein.