用AI统一调度实验室全流程，加速药物发现
Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
- 构建端到端自动化系统，整合仪器、机器人与AI决策
- 集成NVIDIA BioNeMo等模型，提升分子互作预测能力
- 适合药企研发团队与自动化实验平台开发者
自驱动实验室正通过自动化与AI引导实验变革药物发现，但面临工作流协调复杂、设备与AI模型集成困难、数据管理低效等问题。Artificial提出一套全面的编排与调度系统，统一实验室运作，自动化实验流程，并融合AI驱动决策。通过集成NVIDIA BioNeMo等AI/ML模型，实现分子相互作用预测与生物分子分析，提升药物发现效率。系统实时协调仪器、机器人与人员，优化实验流程，增强可重复性，推动数据驱动研究进展。
原文摘要 · Abstract (English)
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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