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

Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs Y ao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller a a Artificial Inc.,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 e fficiently. 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. Introduction The landscape of drug discovery has long been characterized by a multitude of challenges, including the high costs of research and development, lengthy timelines, and a significant rate of failure during clinical trials (Blanco-Gonzalez et al., 2023; Udegbe et al., 2024; Khanna, 2012; Mo ffat et al., 2017).