mlsanityData to 14 Oct 2026

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

arXiv 2504.00986 ↗First posted 1 Apr 2025cs.SE cs.AIcited by 3

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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.

References · 1

  1. 2024BioNeMo Framework: a modular, high-performance library for AI model development in drug discoverycited by 10

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Citing papers per year: 2025: 3.

3 papers.