AI-Enhanced Anticancer Therapeutics

The increasing complexity of cancer biology and therapeutic response has highlighted the limitations of traditional, reductionist approaches to anticancer drug development and therapeutic translation. Precision oncology increasingly requires integrative strategies capable of linking heterogeneous clinical, molecular, phenotypic, and functional data to actionable therapeutic insights, including rational drug prioritization, repurposing, and combination strategies. While artificial intelligence (AI) has been explored in biomedical and clinical research for decades, recent advances in AI architectures, methodologies, and computational scalability have re-ignited interest in its application within clinical oncology and translational cancer research. These developments enable more effective integration and interpretation of multi-dimensional patient and preclinical data at a scale and resolution that was previously impractical. In this context, AI has emerged as a powerful enabling framework to accelerate anticancer drug development pipelines, support translational pharmacology, and identify context-specific tumor vulnerabilities that may be pharmacologically exploited to improve therapeutic development and clinical outcomes.The goal of this Research Topic is to highlight AI-enhanced approaches that accelerate translational anticancer drug development and improve the therapeutic impact of precision oncology. Despite rapid methodological advances in AI tools and computational frameworks, there remains a critical need for contributions that move beyond model benchmarking and prediction accuracy and instead generate biologically interpretable, clinically relevant insights that can inform therapeutic discovery, prioritization, and clinical translation. This Research Topic aims to bring together studies demonstrating how AI can integrate multi-dimensional clinical and translational data, identify context-specific tumor vulnerabilities and resistance mechanisms, and support rational development strategies such as drug repurposing, combination therapy optimization, and dose–schedule refinement. By emphasizing patient relevance, mechanistic plausibility, and translational applicability, this collection seeks to promote AI-enabled development pipelines that connect patient-derived evidence and preclinical validation with actionable therapeutic hypotheses, ultimately strengthening the role of AI as a catalyst for more efficient, evidence-driven anticancer drug development and clinical trial translation.This Research Topic welcomes original research articles, brief research reports, and reviews focusing on AI-enhanced translational anticancer drug development and therapeutics in clinically relevant and translational oncology settings. Relevant themes include, but are not limited to: AI-assisted integration of multi-dimensional clinical and translational datasets to enable therapeutic discovery and prioritization; AI-supported strategies for anticancer drug repurposing; optimization of combination therapies and dose–schedule regimens; identification of resistance mechanisms and context-specific tumor vulnerabilities that can be pharmacologically exploited; and AI-enabled approaches that strengthen the preclinical-to-clinical development pipeline, including trial enrichment and translational study design. Contributions should emphasize patient relevance, translational applicability, and biological interpretability, rather than purely methodological innovation or basic mechanistic exploration. Studies demonstrating how AI improves evidence-driven therapeutic development and accelerates clinical translation in precision oncology are particularly encouraged.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Nikolas Dietis, Constantine Dovrolis, Bekir Cinar, Ph.D., Eswar Shankar Source: https://www.frontiersin.org/research-topics/78652/ai-enhanced-anticancer-therapeutics