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

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.

@article{fehlis2025_2504.00986,
  title={ Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs },
  author={ Yao Fehlis and Paul Mandel and Charles Crain and Betty Liu and David Fuller },
  journal={arXiv preprint arXiv:2504.00986},
  year={ 2025 }
}