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

• URL: http://arxiv.org/abs/2504.00986v1
• Date: Tue, 01 Apr 2025 17:22:50 GMT
• Title: Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
• Authors: Yao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller,
• Abstract summary: Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation.<n>But they face challenges in orchestrating complex, integrating diverse instruments and AI models, and managing data efficiently.<n>Artificial addresses these issues with a comprehensive orchestration and scheduling system.<n>It unifies lab operations, automates, and integrates AI-driven decision-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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