Discovery and Development of New Anticancer Drugs and Delivery Systems: Special Emphasis on Artificial Intelligence, Machine-Learning, and Big Data Analysis

Dear Colleagues,

This Special Issue invites cutting-edge contributions at the interface of discovery science and preclinical translation for novel anticancer drugs and delivery systems, with a particular focus on how artificial intelligence (AI), machine learning, generative large-language models (LLMs), and big-data analytics accelerates this journey. In addition to the synthetic small molecules and biologics, we particularly encourage submission on manuscripts related to natural product-based potential anticancer agents and delivery systems, including strategies that use computational tools to optimize their discovery, derivatization, and formulation. Moreover, we welcome studies that leverage multi-omics integration for target and biomarker discovery; AI-assisted compound design, including quantitative structure activity relationship (QSAR), generative models, and absorption, distribution, metabolism, excretion, and toxicology (ADMET) prediction; and model-informed strategies that connect in silico insights with pharmacology, toxicology, and pharmacokinetics/pharmacodynamics in relevant preclinical models (organoids, patient-derived xenografts, and immune-competent systems). We are especially interested in smart delivery platforms, such as nanoparticles, exosomes, antibody-drug conjugates, and responsive biomaterials, that are optimized by computational modeling for tumor penetration, spatiotemporal release, and immune modulation.

Submissions may also address digital twins, interpretable AI for mechanism elucidation, robustness and bias mitigation, findable, accessible, interoperable, and reusable (FAIR) data practices, and regulatory-science considerations for reproducible pipelines. We particularly welcome contributions that critically appraise current limitations and challenges, for example, the issue of data quality and standardization, model generalizability, translational gaps between preclinical models and human disease, and regulatory or ethical barriers, and that propose concrete future directions for bringing AI-enabled drug discovery and delivery, including natural product-based interventions, closer to clinical impact. Studies that couple imaging and theranostics with predictive modeling, or that present validated workflows, datasets, and benchmarks enabling community reuse, are strongly encouraged. 

By foregrounding rigorous validation and bidirectional feedback between wet-lab experiments and computational predictions, this Special Issue aims to showcase practical, scalable methodologies that shorten the path from laboratory hypotheses to preclinical proof-of-concept and patient-ready translation. We welcome original research, systematic, narrative or scoping reviews, and perspectives that chart ambitious yet actionable roadmaps for translational oncology.

Dr. Rajeev K. Singla
Dr. Bairong Shen
Prof. Dr. Anupam Bishayee
Guest Editors

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