Molecular Insights Unveiled: A Hybrid Neural Network Model GNN-CNN for Drug Discovery

The drug discovery process is well known to be lengthy and costly, making it imperative that new approaches are explored to increase the efficiency with which novel therapeutic candidates can be discovered. To the best of our knowledge, this is among the first that employed an advanced hybrid model integrating Graph Neural Networks (GNN) and Convolutional Neural Networks (CNN), in both drug repositioning and discovery. The powerful ability of the architecture to predict drug-target interactions outperforms other models owing to its combination of GNN and CNN methodology. The hybrid model was trained and validated on large datasets, performing better than standalone GNN, CNN, or traditional machine learning models. Hybrid GNN-CNN showed superior classification accuracy of 92% and high precision, recall, and F1- scores demonstrating the model to be robust and reliable. An in-depth analysis of confusion matrices, ROC curves, and training-validation loss accuracy plots showed the model's effective learning and generalization capabilities. Practical case studies further demonstrated the model's potential to identify novel therapeutic applications for existing drugs, emphasizing its significant implications for expediting drug discovery and addressing unmet medical needs. This research lays a robust foundation for integrating advanced computational techniques into pharmaceutical development, presenting a promising path toward more efficient and accurate drug discovery processes. Future efforts will focus on enhancing dataset diversity, optimizing model architecture, and experimentally validating the predicted drug candidates to ensure their clinical relevance and efficacy.

I. Introduction

Drug discovery and development processes typically take more than 10 years, on average, at a cost of billions of dollars. We will discuss some of the common high-throughput screening approaches in traditional lead optimization, and how they contribute to increased late-stage clinical attrition rates from Efficacy Safety issues. With these challenges, repositioning or repurposing drugs has gained momentum as a more attractive choice. To the best of my knowledge, this is a way to identify new uses for drugs already shown to be safe, in other words, that gets us years ahead around safety and therefore time and money. Successful drug repositioning examples include sildenafil, a compound initially developed for angina that proved to have utility in treating erectile dysfunction, and thalidomide (known originally as an “anxiolytic” sedative) is now being investigated once again for multiple myeloma. In modern drug discovery, and the continual push towards drug repositioning via integrated computational approaches nowadays silico techniques have been increasingly useful.[1]. Their effectiveness has also been enhanced by the growing provision of biological and chemical data. Despite this, GNNs and CNNs have demonstrated exceptional potential in molecular graph generation-based DTI prediction. For example, GNNs are well-suited to graph-structured data types such as molecular graphs of atoms (stored as nodes), bonds (as edges), and their angular arrangements (degree measurements). CNN (Convolutional Neural Networks) are well suited for extracting features from grid-like data structures, such as images, which makes them an attractive approach to be used in the derived molecular type analysis and model building[2]. We propose a hybrid GNN-CNN model towards this end that leverages the benefits of both, by employing GNN for modeling complex relationships in molecular graphs and CNN for high-level feature extraction to improve predictive accuracy. Built on a hybrid model that will leverage deep learning and predictive modeling, it will glean insights from the vast molecular data space to connect drugs and mechanisms of action. This integrated method, owing to the complementary characteristics of GNNs and CNNs will almost certainly complement both already existing identification as well prediction difficulties for potent drug candidates along with repositioning opportunities. This work aims to establish a model for GNN-CNN method-based drug repositioning or discovery, with retrieval of networks in pathway cooperation. In simple terms, this will be used to develop a novel architecture that combines the strengths of GNN and CNN models and then train it using vast public datasets for drug-target interactions as well as molecular properties to evaluate its ability across various bench-marking tasks such as predicting novel drugs candidates or drug repositioning opportunities[1]. We also sought to interrogate the transparency of model predictions and their biological relevance, providing mechanistic information as to how these drugs are functioning.[3]. This work is expected to be influential in transforming drug discovery through enhanced predictive capability which leads to increased success and increases novel therapeutic applications for unmet medical needs resulting from better patient outcomes. If a model that could use GNN-CNN to develop drugs were created, the aforementioned stages would take less time while resources are conserved and effective treatments finally reach patients[2]. The work presented here will enable further development of the field and its consequent implications that are often linked broadly to health improvements, and diffuse global disease burden alleviation.