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By learning from multimodal molecular representations through advanced training techniques, LLMs address traditional limitations in molecular research, including expert dependency and limited experimental scalability. We analyze strategies for cross\u2010modal alignment and domain adaptation that enable LLMs to interpret complex molecular semantics. This deep semantic understanding translates into broad versatility across key downstream applications, encompassing retrieval tasks like molecule\u2010text matching, prediction tasks like reaction outcome inference, and generative tasks including zero\u2010shot molecule design. Crucially, we highlight how LLMs integrate with robotic platforms to establish closed\u2010loop autonomous discovery systems, where AI agents automate hypothesis generation, experimental planning, and iterative validation. 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