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

arXiv: https://arxiv.org/abs/2504.00986 · HTML: https://mlsanity.com/paper/2504.00986/

## 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.

## Builds on (most cited first, 1 of 1)

- [BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery](https://mlsanity.com/paper/2411.10548/) (2024)

## Built on by (most cited first, 3 of 3)

- [Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle](https://mlsanity.com/paper/2507.09023/) (2025)
- [Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents](https://mlsanity.com/paper/2505.21534/) (2025)
- [Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation](https://mlsanity.com/paper/2507.17852/) (2025)

## 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.