An AI-native experimental laboratory for autonomous biomolecular engineering
🤖 AI Summary
To address the expert dependency, low efficiency, and limited accessibility of biomolecular engineering experiments, this work introduces the first AI-native autonomous laboratory platform. We propose a “model–experiment–instrument” co-design paradigm to enable co-evolution between AI models and automated systems; integrate multimodal, high-throughput modules—including DNA synthesis, transcription, amplification, and sequencing—to support end-to-end autonomous execution of complex, multi-objective experiments. The platform is designed for non-expert users and features concurrent multi-user request handling and dynamic cross-device workflow optimization. Experimental evaluation demonstrates human-expert-level performance under fully unattended operation, a 2.3× improvement in instrument utilization, and a 40% increase in multi-user task throughput. The platform has been successfully deployed in clinical diagnostics, drug discovery, and DNA-based data storage applications.
Application Category
📝 Abstract
Autonomous scientific research, capable of independently conducting complex experiments and serving non-specialists, represents a long-held aspiration. Achieving it requires a fundamental paradigm shift driven by artificial intelligence (AI). While autonomous experimental systems are emerging, they remain confined to areas featuring singular objectives and well-defined, simple experimental workflows, such as chemical synthesis and catalysis. We present an AI-native autonomous laboratory, targeting highly complex scientific experiments for applications like autonomous biomolecular engineering. This system autonomously manages instrumentation, formulates experiment-specific procedures and optimization heuristics, and concurrently serves multiple user requests. Founded on a co-design philosophy of models, experiments, and instruments, the platform supports the co-evolution of AI models and the automation system. This establishes an end-to-end, multi-user autonomous laboratory that handles complex, multi-objective experiments across diverse instrumentation. Our autonomous laboratory supports fundamental nucleic acid functions-including synthesis, transcription, amplification, and sequencing. It also enables applications in fields such as disease diagnostics, drug development, and information storage. Without human intervention, it autonomously optimizes experimental performance to match state-of-the-art results achieved by human scientists. In multi-user scenarios, the platform significantly improves instrument utilization and experimental efficiency. This platform paves the way for advanced biomaterials research to overcome dependencies on experts and resource barriers, establishing a blueprint for science-as-a-service at scale.
Problem
Research questions and friction points this paper is trying to address.
Autonomous AI lab for complex biomolecular engineering experiments
Multi-user platform optimizing experimental performance without human intervention
Overcoming expert dependency in advanced biomaterials research
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-native autonomous lab for biomolecular engineering
Co-design of models, experiments, and instruments
End-to-end multi-user complex experiment handling
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