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arXiv · 2504.00986

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

Yao Fehlis·Paul Mandel·Charles Crain·Betty Liu·David Fuller
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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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Yao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller. 2025-04-01. Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs. https://arxiv.org/abs/2504.00986

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