LLM Agents for Scientific Discovery Guide

Overview

A curated survey of how LLM-based agents are being applied to scientific discovery, with a focus on biomedical research. Covers hypothesis generation, experiment design, lab automation, literature synthesis, and multi-agent scientific collaboration. Tracks papers, tools, and frameworks across the spectrum from fully autonomous to human-in-the-loop systems.

Landscape

LLM Agents for Scientific Discovery
├── Hypothesis Generation
│   ├── Literature-based (gap identification)
│   ├── Data-driven (pattern discovery)
│   └── Analogy-based (cross-domain transfer)
├── Experiment Design
│   ├── Protocol generation
│   ├── Parameter optimization
│   └── Control selection
├── Lab Automation
│   ├── Robot control (self-driving labs)
│   ├── Equipment programming
│   └── Data collection orchestration
├── Analysis & Interpretation
│   ├── Statistical analysis
│   ├── Visualization
│   └── Result interpretation
└── Communication
    ├── Paper writing
    ├── Presentation generation
    └── Peer review simulation

Key Systems

System	Domain	Capability
AI Scientist	ML/AI	Full paper generation pipeline
ChemCrow	Chemistry	Tool-augmented chemical reasoning
Coscientist	Chemistry	Autonomous experiment execution
BioPlanner	Biology	Experiment protocol generation
MedAgent	Medicine	Clinical trial analysis
GenAgent	Genomics	Gene expression analysis
DrugAgent	Pharma	Drug interaction prediction