There has been a recent surge of interest in LLM agents that propose new scientific ideas. In this talk, Tom Hope will present recent work on AI models and systems that harness the scientific literature to help researchers identify inspirations and hypothesize directions grounded in literature. This includes (1) Scimon, which was the first work to explore LLMs for scientific hypothesis generation grounded in papers; (2) Scideator, our human-AI system for helping researchers interact with literature as a source of inspiration; and (3) CHIMERA, a dataset of scientific idea recombinations which can be used for training and evaluating systems that generate inspirations based on recombination of concepts from literature. As part of the talk, we will also discuss assessing and enhancing the novelty of scientific ideas.