Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease

Abstract

Herein, a two-step de novo approach was developed for the prediction of piperine targets and another prediction of similar (piperine) compounds from a small molecule library using a deep-learning method. Deep-learning and neural-network approaches were used for target prediction, similarity searches, and validation. The present approach was trained on records containing the data. The model attained an overall accuracy of around 87.5%, where the training and test set was kept as 70% and 30% (17 226/40 197), respectively. This method predicted two targets (MAO-A and MAO-B) and 101 compounds as piperine derivatives. MAO-A and MAO-B are important drug targets in Parkinson's disease. Validation of this method was also performed by considering piperine and its targets (monoamine oxidase A and B) using molecular docking, dynamics simulation and post-simulation analysis of all the selected compounds. Rasagiline, lazabemide, and selegiline were selected as controls, which are already FDA-approved drugs against these targets. Molecular docking studies of the FDA-approved drugs and the compounds we predicted using DL and neural networks were carried out against MAO-A and MAO-B. Using the molecular docking's scoring function, molecular dynamics simulation and free energy calculations as extended validation methods, it was observed that the compounds predicted herein possessed excellent inhibitory effects against the selected targets. Thus, deep learning may play a very effective role in predicting the potential compounds, their targets and can play an expanded role in computer-aided drug approaches.