Computational Approaches in Natural Product Drug Discovery and Development

Natural products remain a major source of new therapeutic agents, and many clinically important drugs originate from bioactive scaffolds refined through modern chemistry. Traditional discovery approaches rely on empirical knowledge and labour-intensive extraction, and they face challenges such as variability in source materials, limited standardisation, and ethical considerations. Advances in computational science now create new opportunities for discovery and development. Cheminformatics, artificial intelligence, and network pharmacology provide rapid screening, predictive modelling, and mechanistic interpretation. The integration of genomics, transcriptomics, proteomics, and metabolomics creates a systems-level perspective on biosynthetic pathways and molecular complexity. This perspective strengthens analytical accuracy, reduces material requirements, and supports sustainable innovation. This review describes the evolution of natural-product drug discovery in the computational era and highlights the role of digital technologies in modern product development.