Using machine learning approaches in drug development problem in bioinformatics

Humans are at the center of the drug research and development process. It is aimed to help the patient overcome his illness and improve his quality of life. In the drug development process, innovative drugs are aimed to be effective, reliable and treatments that will be offered to patients as soon as possible. However, the discovery of a drug and putting it into the service of medicine requires time consuming and high cost. In recent years, thanks to the development of information technologies and bioinformatics-based applications, progress has been made in moving this process to the clinical stage with less cost and quickly. In this thesis, it is aimed to detect molecules that can be drug candidates for the treatment of Type-2 diabetes by using DPP-4 inhibitors and with the help of machine learning approaches. The data obtained from the ChEMBL database were analyzed with 10 machine learning algorithms and artificial neural network model. In comparison of the performances of the models, the Root Mean Square Error (RMSE) criteria were evaluated. As a result of the application, it has been seen that the machine learning approaches that produce the best predictions are Random Forest and a single layer feedforward neural network. It has been observed that these two methods give predictive results close to each other. In the evaluation of the performances of the models, the Random Forest model was chosen as the optimum model because it showed higher performance than the root mean square error value, which is the most common criterion in the literature. According to the results of this study, it has been seen that using the Random Forest approach produces good results in detecting molecules that can be drug candidates for the treatment of Type-2 diabetes.