Network-assisted Discovery and Mechanistic Validation of Bioactive Peptides, Natural Products, and Small Molecules in Pathological Processes

The discovery and mechanistic exploration of bioactive peptides, natural products, and small molecules form the cornerstone of pharmacological and toxicological innovation. While computational strategies such as network pharmacology, virtual screening, and network toxicology have revolutionized hypothesis generation by mapping complex biological interactions, their predictive power is limited without experimental corroboration. Current trends highlight an urgent need to align such computational tools with empirical studies that validate therapeutic efficacy, mechanistic plausibility, and safety. Numerous studies have explored computational design pipelines, yet reproducibility remains a major challenge. The disconnect between in silico predictions and in vivo outcomes underscores a critical knowledge gap: how to translate computational insights into clinically relevant mechanisms. Addressing this gap requires integrating experimental methods that confirm targets, elucidate pathways, and characterize toxicological profiles across complex disease systems.This Research Topic aims to bridge the gap between computational hypothesis generation and experimental validation for bioactive peptides, natural products, and small molecules. Its primary objective is to encourage research that couples predictive computational modeling with mechanistic and toxicological verification. Key goals include elucidating molecular mechanisms underlying therapeutic actions, confirming computationally predicted interactions through in vitro and in vivo models, and improving reproducibility in translational pharmacology. In particular, this collection will test hypotheses emerging from network based predictions in contexts such as neurodegeneration, metabolic and digestive dysfunction, urological diseases, exposure to emerging pollutants, and clinical complications associated with surgery or lifestyle. By emphasizing synergy between predictive analytics and empirical science, the Research Topic seeks to create a standardized framework for experimentally validated network pharmacology.This Research Topic encompasses the experimental validation and mechanistic interpretation of computational predictions, with a focus on translational outcomes across disease systems. Computational analyses may be included only when supported by robust wet lab or clinical data. To gather further insights into the integration of computational prediction and experimental validation, we welcome articles addressing, but not limited to, the following themes:• Pathological mechanisms validated experimentally for bioactive peptides, natural products, and small molecules predicted computationally• Experimental pathology of neurodegenerative, metabolic, digestive, or urological diseases guided by network pharmacology or toxicology• Toxicopathological assessments of emerging contaminants (e.g., microplastics, PFAS) informed by computational predictions• Mechanistic validation of disease models integrating metabolomic or biomarker data with clinical prediction analyses (e.g., esophageal anastomotic leakage)• Multi targeted therapeutic or toxicological interactions explored through combined network assisted and histopathological approaches Topic editors: Jie Zhang, Carlos L Cespedes-Acuña, Tianzhu Guan Source: https://www.frontiersin.org/research-topics/81014/network-assisted-discovery-and-mechanistic-validation-of-bioactive-peptides-natural-products-and-small-molecules-in-pathological-processes