AI-Assisted Design of Heterocyclic Scaffolds
Corresponding author: [email protected]
Abstract
Artificial intelligence (AI) is transforming modern drug discovery by enabling the rapid design and optimization of biologically active molecules. Among these, heterocyclic scaffolds occupy a central position because they are present in the majority of approved pharmaceuticals and exhibit diverse biological activities. Conventional approaches to heterocyclic scaffold design are often time-consuming, expensive and dependent on extensive experimental screening, leading to high failure rates during drug development. The integration of AI techniques, including machine learning, deep learning, generative models and predictive analytics, has significantly accelerated the identification of novel heterocyclic compounds with improved pharmacological properties. AI-driven platforms can analyse large chemical and biological datasets, predict molecular properties, estimate toxicity, optimize synthesis pathways and identify promising lead molecules with greater accuracy than traditional methods. Furthermore, the combination of chemistry, biology and computer science has enabled the development of intelligent computational tools that support rational drug design and personalized medicine. This review discusses the evolution of drug discovery, the limitations of conventional heterocyclic scaffold design, the emergence of AI in medicinal chemistry and the growing role of computational intelligence in pharmaceutical research.
Author Affiliations
- 1 Associate Professor, Department of Pharmaceutical Chemistry, Rashtriya College of Pharmacy, Hatnoor, Maharashtra, India.
- 2 Assistant Professor, Department of Pharmacology, Dr. Kolpe Institute of Pharmacy, Kolpewadi, Maharashtra, India.