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

2025-04-01

AI Summary

To address challenges in autonomous laboratories for drug discovery—including poor multi-instrument coordination, weak AI model integration, and inefficient data management—this paper proposes a full-stack orchestration and scheduling system tailored for self-driving labs. We introduce a novel “whole-laboratory-granularity” unified scheduling framework enabling real-time human–robot–instrument collaboration; tightly integrate foundation models (e.g., BioNeMo) into the experimental decision-making loop to overcome the procedural rigidity of conventional automation platforms; and implement a scalable architecture comprising a microservice-based dynamic workflow engine, robotic middleware (ROS/RAPID), a semantic data lake, and a real-time event bus. In a real-world target validation use case, the system reduces experimental cycle time by 67%, achieves 99.2% reproducibility accuracy, and supports millisecond-level task scheduling across hundreds of heterogeneous instruments.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Orchestrating complex workflows in self-driving labs
• Integrating diverse instruments and AI models efficiently
• Managing data for accelerated drug discovery

Innovation

• Unified orchestration system for lab operations
• AI-driven workflow automation and decision-making
• Real-time coordination of instruments and personnel