Using Computational Intelligence to Connect Data and Drug Design in Medicinal Chemistry: A Review

The landscape of drug discovery is being rapidly transformed by the integration of computational intelligence (CI) techniques with big data resources in medicinal chemistry. Traditional drug development methods are often time-consuming, costly, and prone to high attrition rates. In contrast, data-driven and algorithmic approaches-powered by machine learning, deep learning, and hybrid models-enable rapid, precise, and predictive decision-making across all phases of drug design. This review provides a comprehensive overview of how CI techniques, including supervised and unsupervised learning, convolutional and recurrent neural networks, evolutionary algorithms, and fuzzy logic systems, are reshaping drug discovery pipelines. We explore the role of massive chemical and biological databases such as PubChem, ChEMBL, DrugBank, and the Protein Data Bank, while also highlighting the importance of data quality, curation, and standardization. The integration of computational tools in drug discovery is discussed across key stages-target identification, hit discovery, lead optimization, and de novo molecular design-supported by examples such as AlphaFold, Atomwise, and In silico Medicine. This review aims to offer a holistic understanding of how computational intelligence, when combined with robust data infrastructure, can significantly accelerate the discovery and development of safer, more effective drugs.