Structure-Based and AI-Assisted Identification of AGPS Inhibitors for Glioma via Integrated Docking, Molecular Dynamics, and Binding Affinity Screening.

Cancer remains among the most aggressive and treatment-resistant diseases, with a persistent failure of therapeutic strategies. Addressing the bottlenecks in cancer drug discovery, we present a feature-driven AI-integrated pipeline designed for systematic identification of repurposable drug candidates against druggable targets across diverse types of cancers. As proof, we applied this pipeline to glioma. We utilized Gen AI to identify an antiglioma target, alkylglycerone phosphate synthase (AGPS), a key enzyme in tumor metabolism and progression. Using a deep learning model, we screened over 5,76,510 compounds from the life chemicals high-throughput screening database for their potential to inhibit AGPS. ROC analysis of top candidates identified through graph neural network modeling and Glide docking yielded an AUC of 0.89, supporting the model's ability to discriminate between active and inactive compounds. Top-scoring candidates were subjected to rigorous molecular dynamics (MD) simulations to assess the binding stability. Among them, F2881-0267 emerged with favorable drug-like properties. To evaluate the binding free energy landscape, we developed a hybrid deep learning model combining 3D convolutional neural networks and multilayer perceptrons. This framework integrates spatial features, molecular interaction fingerprints, and physics-based energy descriptors derived from MD trajectories. Our findings showcase the potential of this AI transformative model to streamline drug discovery workflows, which can be applied to other therapeutically relevant targets similar to AGPS.