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.