Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912304024518656 |
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| author | Fehlis, Yao Mandel, Paul Crain, Charles Liu, Betty Fuller, David |
| author_facet | Fehlis, Yao Mandel, Paul Crain, Charles Liu, Betty Fuller, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00986 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs Fehlis, Yao Mandel, Paul Crain, Charles Liu, Betty Fuller, David Software Engineering Artificial Intelligence 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. |
| title | Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00986 |