Generative AI for Drug Molecule Discovery

This research paper explores the role of Generative Artificial Intelligence in accelerating drug molecule discovery. Traditional drug discovery processes are often time-consuming and expensive, requiring extensive laboratory experimentation. This study reviews how modern AI techniques such as Transformer models, Diffusion models, and Reinforcement Learning can generate and optimize molecular structures efficiently. Transformer-based architectures like ChemBERTa and MolFormer help understand chemical structures and generate valid molecular sequences. Diffusion models focus on creating accurate 3D molecular conformations that maintain chemical and spatial properties. Reinforcement Learning frameworks guide molecular generation toward desired pharmaceutical properties such as stability, efficacy, and reduced toxicity. The paper highlights how integrating these AI approaches can significantly reduce the time and cost involved in drug discovery while enabling researchers to explore a larger chemical space. This work demonstrates the growing importance of artificial intelligence in modern pharmaceutical research and molecular design.