Prediction of Drug Classes with a Deep Neural Network using Drug Targets and Chemical Structure Data

Abstract

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 high-dimensional 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 learning-based methods in terms of prediction accuracy.