Prediction of Drug Classes with a Deep Neural Network using Drug Targets and Chemical Structure Data Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in highdimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks  with biological and chemical structure data. Our proposed model outperforms previous learningbased methods in terms of prediction accuracy.

Novel KMeans Clusteringbased Undersampling and Feature Selection for Drug Discovery Applications Drug discovery refers to the process of identification of specificdisease causing proteins and underscores the research efforts to derive a new medication that targets these proteins. As such the drug discovery process entails significant challenges as it is time consuming, data intensive, and involves an expensive developmental process which demands rigorous lab testing with high rates of uncertainty that the given drug will succeed. Therefore, it highlights the crucial need for machine learning methods to automate and hasten the drug discovery pipeline for improved healthcare and assist clinicians to make informed decisions for invitro testing. However, most realworld biomedical datasets suffer from statistical illconditioning issues such as the class imbalance problem where the fewer class of potential drug candidate protein conformations are overshadowed by the larger proteinpool of nondrug candidates. Hence, this leads to erroneous conclusions when machine learning techniques are directly employed for datalearning and classification purposes. Therefore, this work takes a revolutionary stance to counter the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential drug candidate molecular conformations for the target proteins ADORA2A and OPRK1 and subsequently reduces the failure rates of the drug discovery process. Experimental evaluation of the proposed machine learning methodologies further substantiates the effectiveness of our approach for drug discovery process.