A novel ligand-based convolutional neural network for identification of P-glycoprotein ligands in drug discovery

P-glycoprotein (P-gp) is a crucial drug transporter in several drug-resistant cases that are serious challenges in drug delivery and cancer treatment. Existing computational approaches mostly depended on small datasets for predicting P-gp interactions. To overcome these limitations, this paper proposes a Novel Ligand-based Convolutional Neural Network (NLCNN) framework to classify and predict P-gp substrates with high accuracy. The model is trained on a curated dataset of 197 P-gp substrates, integrating molecular docking and ligand-based deep learning methods for further predictive improvement. Experimental evaluations show that the NLCNN, on average, achieves prediction accuracy of 80%, which is 19-24% higher in precision and recall metrics compared to conventional Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). Here, CNN is considered as the major model, whereas the SVM is emphasized as a baseline classifier. The proposed FLCNN algorithm obtains a noticeable accuracy, thus outperforming conventional SVM with a Gaussian RBF kernel. Moreover, by using the X-ray structure of mouse P-gp as a template, a homology model of human P-gp permits accurate molecular docking analysis. The proposed model is implemented in drug discovery and personalized medicine for P-gp interaction prediction. This is a landmark achievement in computational pharmacology as it puts out a powerful, accurate, and simple tool for determining P-gp inhibitors and substrates.