Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects

Abstract

Advanced AI algorithms, notably generative pre-trained transformer (GPT) models, are revolutionizing healthcare and drug discovery and development by efficiently processing and interpreting large volumes of medical data. Specialized models, such as ProtGPT2 and BioGPT, extend their capabilities to protein engineering and biomedical text mining. Our study will contribute to ongoing discussions to revolutionize drug development, leading to a faster and more reliable validation of new therapeutic agents that are crucial for healthcare advancement and patient outcomes. GPT models, such as MTMol-GPT, are robust, generalizable, and provide important information for developing treatments for complicated disorders. SynerGPT utilizes a genetic algorithm to optimize prompts and select drug combinations for testing based on individual patient characteristics. Ligand generation for specific target proteins with potential drug activity is a significant stage in the drug design process, which enhances the quality of the synthesized compounds and augments the precision of capturing chemical structures and their activity correlations, highlighting the model's creativity and capability for innovative ligand design. Despite these advancements, there are still problems with the data volume, scalability, interpretability, and validation. Ethical considerations, robust methods, and omics data must be successfully integrated to develop AI for drug discovery and ensure successful deployment. In summary, these models significantly influence drug research and development, specifically in the earlier stages from initial target selection to post-marketing surveillance for medication safety monitoring.