Using QSAR calculation of amid derivatives for the treatment of Parkinson’s disease

Parkinson's disease is one of the most common neurological disease is considered malicious. Parkinson's disease is a chronic condition and always in progress. The goal of treatment for patients with symptoms but not eliminate the symptoms under control [1]. On the pattern, quantitative structure–activity relationship (QSAR) study has been done on a series of Amid derivatives for the treatment of Parkinson’s disease. The purpose of QSAR study is to find a relation between the composition or structure of a compound with its bio or chemical activity, in order to design a new compound with expected properties or predict the properties of an unknown compound. Up to now, a lot of successful applications have been reported in many different types of cases, e.g., medicine design, environmental chemistry exploration, pesticide searching, etc [2].The artificial neural networks (ANNs) are known as a good method in expressing highly non-linear relationship between the input and output variables, hence, greater interests were attracted in applying them to the pattern classification of complex compounds [3]Genetic algorithms (GAs) were introduced by Holland. They mimic nature’s evolutionary method of adaptation to a changing environment. GAs are stochastic optimization methods hat provide powerful means to perform directed random searches in a large problem space as encountered in chemometrics and drug design . In multiple linear regression (MLR), for a given data set consisting of a target variable and M descriptors for n compounds, a model is made with good fitting to define the combination of m descriptors (m< M) on target variable. Running through all combinations usually is too time-consuming. Therefore, several approximate methods have been proposed for this reason, but none of them guarantied to find very best combination in all cases.