Cancer persists as a leading cause of mortality worldwide, underscoring the urgent need to decipher its molecular complexity for improved detection, prognosis, and treatment. The integration of artificial intelligence (AI), machine learning (ML), and advanced bioinformatics is currently revolutionizing our understanding of cancer biology, uncovering novel biomarkers, elucidating therapeutic response mechanisms, and accelerating the development of personalized therapies.

Cancer cells exhibit intricate genomic, epigenetic, and proteomic variations that interact dynamically with the tumor microenvironment. Harnessing multi-omics data through state-of-the-art computational tools enables the identification of key oncogenic drivers, novel drug targets, and resistance pathways. AI- and ML-based methods are redefining the landscape of drug discovery by enabling structure-based drug design, high-throughput virtual screening, and the precision optimization of small-molecule inhibitors.

This Research Topic welcomes pioneering original research and comprehensive reviews that highlight the role of computational and AI methodologies in elucidating cancer pathogenesis and enhancing the rational development of anti-cancer agents. We are particularly interested in studies that translate complex molecular data into clinically actionable insights for targeted therapy, paving the way for precision oncology.

We invite contributions spanning the following areas (but not limited to):

- AI and Machine Learning Applications in Cancer Research

- Computational Strategies for Biomarker and Therapeutic Target Identification

- Design and Optimization of Small-Molecule Cancer Inhibitors

- Integration of Multi-Omics and Big Data Analytics

- Predictive Modeling of Drug Response and Resistance Mechanisms

- Translational Informatics and Clinical Implementation

- Emerging Computational Tools, Resources, and Databases

Submissions emphasizing AI-driven discoveries, innovative algorithms, atomistic insights, and the development of next-generation anti-cancer therapies are especially encouraged. With this collection, we aim to foster multidisciplinary dialogue and lay the foundation for future breakthroughs in precision oncology.

Are you interested in contributing a manuscript to this Research Topic?

Decoding Cancer Complexity with Artificial Intelligence and Molecular Level Studies: Informatics-Driven Approaches for Therapeutic Innovation