Democratizing AI scientists using ToolUniverse
This work addresses the problem of democratizing AI scientist development for researchers by providing a unified infrastructure, though it appears incremental as it builds on existing concepts of interoperability and tool ecosystems.
The authors tackled the challenge of building AI scientists by developing ToolUniverse, an ecosystem that standardizes tool integration and workflow composition, enabling the creation of an AI scientist that identified a potent drug analog for hypercholesterolemia with favorable predicted properties.
AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid workflows, and lack shared environments that unify tools, data, and analyses into a common ecosystem. In genomics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development; AI scientists require comparable infrastructure. We present ToolUniverse, an ecosystem for building AI scientists from any language or reasoning model across open- and closed-weight models. ToolUniverse standardizes how AI scientists identify and call tools by providing more than 600 machine learning models, datasets, APIs, and scientific packages for data analysis, knowledge retrieval, and experimental design. It automatically refines tool interfaces for correct use by AI scientists, generates new tools from natural language descriptions, iteratively optimizes tool specifications, and composes tools into agentic workflows. In a case study of hypercholesterolemia, ToolUniverse was used to create an AI scientist to identify a potent analog of a drug with favorable predicted properties. The open-source ToolUniverse is available at https://aiscientist.tools.