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Machine learning, data mining and artificial intelligence (AI) based methodshave been used to determine the relations between chemical structure andbiological activity, called quantitative structure activity relationships(QSARs) for the compounds. Pre-processing of the dataset, which includes themapping from a large number of molecular descriptors in the original highdimensional space to a small number of components in the lower dimensionalspace while retaining the features of the original data, is the first step inthis process. A common practice is to use a mapping method for a datasetwithout prior analysis. This pre-analysis has been stressed in our work byapplying it to two important classes of QSAR prediction problems: drug design(predicting anti-HIV-1 activity) and predictive toxicology (estimatinghepatocarcinogenicity of chemicals). We apply one linear and two nonlinearmapping methods on each of the datasets. Based on this analysis, we concludethe nature of the inherent relationships between the elements of each dataset,and hence, the mapping method best suited for it. We also show that properpreprocessing can help us in choosing the right feature extraction tool as wellas give an insight about the type of classifier pertinent for the givenproblem.

Pre-processing in AI based Prediction of QSARs