Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs

April 01, 2025 Β· Declared Dead Β· πŸ› 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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