Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 1 of its 33 references that are in the map. First posted on arXiv 1 Apr 2025.
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1 of 33 references.
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Citing papers per year: 2025: 3.
3 papers.
- 2025Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cyclecited by 6
- 2025Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agentscited by 2
- 2025Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automationcited by 2
- 2025Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation18 Jul 2025
- 2025Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle11 Jul 2025
- 2025Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents23 May 2025