The integration of artificial intelligence (AI) and machine learning (ML) in drug design has the potential to transform small molecule drug discovery in the pharmaceutical industry, enhancing the efficiency and productivity of the drug design and discovery process.
Self-Driving Labs (SDLs) combine automated hardware with computational experiment planning tools to reduce the time between experiments and liberate chemists from routine work, allowing them to focus on bigger, more conceptual problems.
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation.