Introduction
This Thematic Issue focuses on the transformative potential of artificial intelligence in advancing brain tumor drug discovery by integrating deep learning, medical imaging, and cloud-enabled computational approaches. It highlights the urgent need for innovative solutions to address challenges such as poor prognosis in glioblastoma and the limitations imposed by the blood–brain barrier on drug delivery. The proposal emphasizes the convergence of AI-driven medical imaging for accurate tumor characterization, advanced computer-aided drug design techniques for rapid and efficient lead discovery, and scalable cloud infrastructures that enable collaborative and high-performance research. By bridging these domains, the special issue aims to foster interdisciplinary research in precision neuro-oncology, covering areas such as target identification, radiomics-based drug response prediction, generative molecular design, and nanoparticle-based drug delivery systems, ultimately contributing to more effective and personalized therapeutic strategies.
Keywords
Glioblastoma Multiforme (GBM), Blood-Brain Barrier (BBB) Drug Delivery, Deep Learning in Medical Imaging, Multimodal Imaging (MRI/PET/CT), Computer-Aided Drug Design (CADD), AI-Driven Drug Discovery, Cloud Computing in Biomedical Research, Precision Neuro-Oncology
Sub-topics
- Deep Learning Architectures for Brain Tumor Detection and Segmentation
- AI-Powered Molecular Target Identification and Drug Candidate Screening
- Medical Imaging Analytics (MRI/CT/PET) for Therapy Monitoring and Response Prediction
- Cloud-Enabled High-Performance Computing for Computational Drug Design
- Multi-Omics Data Integration and AI-Driven Biomarker Discovery in Glioblastoma
- Explainable AI and Federated Learning for Privacy-Preserving Brain Tumor Drug Discovery
