Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure

Modern medical science has been greatly advanced by the development of new drugs, despite the fact that the process of developing new drugs is costly and time-consuming. An accurate prediction method for the drug-likeness in the early stage of drug discovery is highly desirable, as it will facilitate the discovery process and reduce the overall cost and eventually contribute to human well-being. Based on a central nervous system (CNS) drug dataset, we constructed an artificial neural network (NN) to predict the CNS drug-likeness of a given bioactive compound. We first constructed a simple feed-forward neural network, to learn and predict the possible correlations between twelve physiochemical properties and the CNS drug-likeness. The accuracy of prediction has reached 80%, which has been improved from previous reports. We further constructed a neural network based on chemical structure, and the accuracy has increased to 86%. The successful prediction of the CNS drug-likeness renders this NN a powerful tool for virtual drug screening.