Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
April 01, 2025 Β· Declared Dead Β· π arXiv.org
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Authors
Yao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller
arXiv ID
2504.00986
Category
cs.SE: Software Engineering
Cross-listed
cs.AI
Citations
5
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI/ML models like NVIDIA BioNeMo - which facilitates molecular interaction prediction and biomolecular analysis - Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery.
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