Large Language Models in Drug Discovery: 2025 State-of-the-Art
Large Language Models in Drug Discovery: 2025 State-of-the-Art Overview Large language models have emerged as transformative tools across the entire drug discovery pipeline, from target identification through clinical development, with applications spanning natural language processing of biomedical literature and specialized molecular design. The field has evolved to address the time-consuming and expensive nature of traditional drug discovery, which typically requires over a decade and billions of dollars per approved drug. Key Application Areas 1. Molecular Generation and Design Multimodal Approaches MIT researchers developed Llamole (Large Language Model for Molecular Discovery), which combines LLMs with graph-based AI models to design molecules and generate synthesis plans. The system improved retrosynthetic planning success rates from 5% to 35% by integrating graph diffusion models with natural language understanding. Llamole uses trigger tokens to switch between modules: a...