A Review of Deep Learning in ComputerAided Drug Design Recently, Deep Learning has been applied to many medical domains, such as medical image analysis, bioinformatic, biochemistry, drug design, and so forth, to improve the performance that is superior to traditional computational approaches; especially in computeraided drug design. Many AIdriven drug discovery startups have utilized deep learning methodology to achieve the significant improvement of searching candidate compounds, predicting functions, and so forth. Therefore, using AI to facilitate drug design is the trend in the coming future. In this study, a comprehensive review of the current stateoftheart in ComputerAided Drug Design using deep learning methods is presented. Meanwhile, the challenges and potential of these methods are also highlighted

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.