Advancements and Challenges in Chemoinformatics: From Drug Discovery to Natural Products Research

Chemoinformatics, an interdisciplinary field combining chemistry, computer science, and information technology, is revolutionising scientific research by using computational methods to analyse, interpret, and predict chemical and biological data. This review explores its pivotal role across drug discovery and natural product research, highlighting key methodologies and challenges. It is critical in drug discovery because it expedites the identification and optimization of drug candidates through techniques such as virtual screening, molecular docking, and quantitative structure-activity relationship (QSAR) studies. It also facilitates the integration of computational chemistry with experimental validation, improving predictions iteratively. Chemoinformatics helps with managing databases, virtually screening bioactive compounds, and figuring out structures through molecular dynamics simulations and metabolomics in the field of natural products research. These tools enable researchers to explore the therapeutic potential of indigenous medicinal plants and other natural sources, facilitating the discovery of novel bioactive molecules with potential pharmaceutical applications. However, challenges such as data quality, computational resources, and ethical considerations persist, particularly in regions with limited infrastructure and expertise. The integration of machine learning and big data analytics promises to further enhance predictive modelling and accelerate discoveries in chemoinformatics. Keywords: Chemoinformatics, Drug Discovery, Natural Products, Research