Drug discovery is a long and costly process, often taking over a decade and billions of dollars. A crucial early step, target identification and validation, involves creating detailed dossiers to assess the viability of potential drug targets. This traditionally time-consuming research process is ripe for disruption, and AI is stepping up to the challenge. New research introduces SwiftDossier, an innovative system that leverages Large Language Models (LLMs) and software agents to automate the creation of these essential documents. Imagine an AI assistant that can scour vast scientific databases, extract key information, and synthesize it into a comprehensive target dossier, complete with a PDF report and PowerPoint presentation. This is the promise of SwiftDossier. The system addresses a key limitation of LLMs: their tendency to generate inaccurate or 'hallucinated' information. SwiftDossier employs a refined Retrieval Augmented Generation (RAG) system. Instead of relying solely on the LLM's internal knowledge, the RAG system retrieves relevant information from trusted sources like PubMed and PMC, enhancing the accuracy and reliability of the generated dossiers. This process ensures that researchers have access to up-to-date, verifiable information, crucial for making informed decisions. SwiftDossier goes beyond text generation by incorporating external tools and databases. It can execute code, retrieve images, and access specialized resources like UniProt and DrugBank. This functionality allows for the creation of dossiers rich in data, covering everything from target gene characteristics and disease pathways to competitive drug landscapes. This represents a significant leap from previous LLM applications in drug discovery, which often focused on narrow tasks like molecule optimization. SwiftDossier offers a more holistic approach, streamlining the entire target assessment workflow. The ability to automatically generate target dossiers offers several potential benefits. It saves researchers valuable time, allowing them to focus on analysis and interpretation rather than manual information gathering. It also helps standardize dossier content, improving consistency across projects. While human expertise remains vital in drug discovery, SwiftDossier serves as a powerful AI assistant, enhancing productivity and accelerating the process. This research paves the way for more sophisticated AI tools that support scientists in complex biomedical tasks. The integration of LLMs with external tools and databases is a promising direction, empowering researchers to tackle the challenges of drug discovery with greater speed and efficiency. The future of medicine may well depend on such innovative applications of AI, leading to faster development of life-saving treatments.