Ó³»­´«Ã½

Large Language Models (LLMs) have demonstrated strong performance in tasks across domains, but struggle with complex chemistry-related problems. Moreover, these models lack access to updated knowledge sources and software, limiting their usefulness in scientific applications. We use LLM-based scientific agents to address this gap by integrating expert-designed tools. ChemCrow is an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design, including novel design. Similarly, MDCrow leverages molecular dynamics tools to automate protein simulation setup, execution, and analysis. While frontier LLMs generally perform better on chemistry tasks, we find that open-source models, when augmented with appropriate tools, can achieve comparable results.