Natural Product-Inspired Drug Discovery: Integrating Computational Screening and Pharmacological Validation

Natural product-inspired drug discovery represents one of the most enduring and fertile paradigms in pharmaceutical research. Historically, many clinically approved drugs have originated from compounds derived from plants, microorganisms, and marine organisms. However, the rediscovery of known scaffolds, limited accessibility of complex structures, and challenges in bioactivity optimization have constrained progress. Recent advances in cheminformatics, artificial intelligence, and virtual screening have reinvigorated this field by enabling the rational exploration of natural product analogues and derivatives. Yet, despite the acceleration of computational predictions, there remains a critical need to establish efficient pipelines that integrate these computational insights with rigorous pharmacological validation, bridging in silico promise with empirical efficacy.This Research Topic aims to foster interdisciplinary dialogue and innovation at the interface of computational chemistry, molecular pharmacology, and natural product research. Specifically, it seeks to highlight strategies that enhance the predictability, efficiency, and translational potential of natural product-derived entities. Contributors are encouraged to address key questions surrounding how virtual screening tools, molecular docking, and predictive modelling can best guide experimental design, how to prioritize compound libraries rooted in natural product scaffolds, and how to experimentally validate computational hypotheses using pharmacodynamic and pharmacokinetic assays. By uniting computational discoveries with experimental verifications, this topic aims to accelerate the identification of bioactive leads with true therapeutic relevance.To gather further insights into the integration of computational and pharmacological methodologies for natural product-inspired therapeutics, we welcome contributions that span the following themes:&#8226 Machine learning and AI-driven virtual screening approaches for natural products&#8226 Structure-activity relationship (SAR) modelling and scaffold optimization of bioactive compounds&#8226 Development of hybrid pipelines integrating cheminformatics and high-content screening&#8226 Pharmacological and toxicological validation of in silico predictions&#8226 Case studies of natural product-inspired drug candidates progressing through early discovery stages&#8226 Technological innovations enhancing natural product accessibility and screening efficiency Topic editors: Anand Gaurav, Dr. Emmanuel Kofi Kumatia, Humberto Fonseca De Freitas Source: https://www.frontiersin.org/research-topics/82658/natural-product-inspired-drug-discovery-integrating-computational-screening-and-pharmacological-validation