The development of precise diagnostic and therapeutic strategies in oncology increasingly depends on identifying robust molecular targets and the rational design of ligands that engage them with high specificity. These ligands ranging from peptides and antibodies to small molecules and aptamers, can be engineered to selectively bind tumor-associated biomarkers, enabling diagnostics and targeted delivery of therapeutic agents or imaging probes.

The explosion of high-throughput multi-omics data has created new opportunities for integrating artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for mining actionable biomarkers from complex datasets. When integrated with bioinformatics pipelines and experimental validation, these approaches offer unprecedented opportunities to uncover extracellular and membrane-associated targets ideal for ligand-guided therapies. Furthermore, structure-based drug design, molecular docking, and ligand-target interaction modeling provide critical insights into binding affinity, selectivity, and therapeutic efficacy.

This Research Topic aims to bring together cutting-edge interdisciplinary advances in bioinformatics, AI-based discovery, and translational cancer research. We welcome original research, systematic reviews, methods, and brief reports that focus on:#

· Identification of cancer biomarkers using multi-omics and AI/ML techniques (e.g., transcriptomics, epigenomics, miRNA, proteomics)

· Computational design and optimization of targeting ligands (peptides, antibodies, small molecules, aptamers)

· Ligand–receptor interaction studies and structural modeling (molecular docking, MD simulations)

· Development of ligand-based diagnostics and therapeutic strategies, including antibody-drug conjugates (ADCs), peptide–drug conjugates, targeted imaging agents, and nanocarrier systems

· Integration of in silico and experimental studies in preclinical or clinical cancer models

· Ligand-based targeting of the tumor microenvironment, exosome pathways, or immune modulatory markers

· Translational case studies involving ligand-target systems in solid tumors

This Topic will be of particular interest to researchers who combine computational discovery pipelines with experimental validation, bridging discovery and application. We encourage contributions from molecular oncologists, computational biologists, medicinal chemists, translational scientists, and clinician-researchers to build a cohesive collection that advances ligand-based strategies for cancer precision medicine.

This Topic will focus on the identification of cancer biomarkers and development of ligand-based diagnostic and therapeutic strategies including peptides, antibodies, small molecules, and aptamers by integrating artificial intelligence, bioinformatics, and experimental validation. We aim to highlight work that bridges discovery and application in precision oncology.

Manuscripts based solely on bioinformatics or computational analysis of public omics databases, without appropriate validation—such as clinical cohort studies, experimental (in vitro or in vivo) confirmation, or rigorous computational validation using independent datasets—are out of scope for this Research Topic.

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

Precision Targeting in Cancer: Biomarker Discovery, AI-Guided Diagnostics, and Translational Therapeutics