Which AI Scientist Suits Your Lab? A Practical Guide

A new generation of general-purpose "AI scientist" tools is emerging, capable of helping with literature reviews, data analysis, hypothesis generation, figure creation, and even manuscript drafting. These agentic systems go beyond narrow tools like AlphaFold by breaking down complex requests into steps and integrating external software.

Nature's Ewen Callaway offers a grounded guide for researchers navigating this rapidly evolving landscape. Key recommendations include:

•⁠ ⁠Start with small, verifiable tasks to build confidence and understand limitations.

•⁠ ⁠Use hypothesis-generating tools (such as Google DeepMind's Co-Scientist) early in a project.

•⁠ ⁠Use more task-specific tools (such as Anthropic's Claude Science) later in the workflow.

•⁠ ⁠Evaluate tools through hands-on experimentation rather than relying on marketing claims.

Real-world examples show biologists using these tools to analyze genomes in minutes (versus months of traditional effort), design antibodies, and test novel hypotheses on immune responses. Despite the promise, adoption remains low — fewer than 20% of labs have fully integrated these systems.

This Ledger Entry expands how readers think about integrating agentic AI into scientific workflows by providing a practical framework for choosing and using general-purpose AI tools — starting with small verifiable tasks, using hypothesis-generation tools early, and task-specific tools later — while emphasizing hands-on evaluation over hype.