The field of artificial intelligence is rapidly advancing in its ability to facilitate scientific discovery. Recent developments have focused on improving the ability of language models to reason, generate hypotheses, and collaborate with other models to solve complex problems. One key area of advancement is in the use of foundation models, which have been shown to be effective in generating novel solutions and improving the performance of other models. Another area of focus is on the development of multi-agent systems, which enable the collaboration of multiple models to achieve a common goal. These systems have been applied to a variety of tasks, including drug discovery, protein structure prediction, and materials science. Notably, the integration of artificial intelligence with scientific domains such as chemistry and biology has led to significant breakthroughs, including the development of new antibiotics and the improvement of protein structure prediction models. Overall, the field is moving towards the development of more general-purpose models that can be applied to a wide range of scientific tasks, enabling faster and more efficient discovery. Noteworthy papers include Agent KB, which introduces a novel Reason-Retrieve-Refine pipeline for agentic problem solving, and ApexOracle, which predicts the antibacterial potency of existing compounds and designs de novo molecules active against strains it has never encountered.