Measuring AI’s capability to accelerate biological research

Measuring AI’s capability to accelerate biological research in the wet lab | OpenAI

December 16, 2025

Measuring AI’s capability to accelerate biological research in the wet lab

GPT‑5 created novel wet lab protocol improvements, optimizing the efficiency of a molecular cloning protocol by 79x.

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Accelerating scientific progress is one of the most valuable ways AI can benefit humanity. With GPT‑5, we’re beginning to see early signs⁠ of this—not only in helping researchers move faster through the scientific literature, but also in supporting new forms of scientific reasoning, such as surfacing unexpected connections, proposing proof strategies, or suggesting plausible mechanisms that experts can evaluate and test.

Progress to date has been most visible in fields like mathematics, theoretical physics, and theoretical computer science, where ideas can be rigorously checked without physical experiments. Biology is different: most advances depend on experimental execution, iteration, and empirical validation in the laboratory.

To help understand how frontier models behave in these settings, we worked with Red Queen Bio, a biosecurity start-up, to build an evaluation framework that tests how a model proposes, analyzes, and iterates on ideas in the wet lab. We set up a simple molecular biology experimental system and had GPT‑5 optimize a molecular cloning protocol for efficiency.

Over multiple rounds of experimentation, GPT‑5 introduced a novel mechanism that improved cloning efficiency by 79x. Cloning is a fundamental molecular biology tool. The efficiency of cloning methods is critical for creating large, complex libraries central to protein engineering⁠, genetic screens⁠, and organismal strain engineering⁠. This project offers a glimpse of how AI could work side-by-side with biologists to speed up research. Improving experimental methods will help human researchers move faster, reduce costs, and translate discoveries into real-world impact.

Because advances in biological reasoning carry biosecurity implications, we conducted this work in a tightly controlled setting—using a benign experimental system, limiting the scope of the task, and evaluating model behavior to inform our biosecurity risk assessments and the development of model- and system-level safeguards, as outlined in our Preparedness Framework⁠.

Experimental results

In this set-up, GPT‑5 autonomously reasoned about the cloning protocol, proposed modifications, and incorporated data from new experiments to suggest more improvements. The only human intervention was having scientists carry out the modified protocol and upload experimental data.

Over the course of multiple rounds, GPT‑5 optimized the cloning procedure to improve the efficiency by over 79x—meaning that for a fixed amount of input DNA, we recovered 79x more sequence-verified clones than the baseline protocol. Most notably, it introduced two enzymes that constitute a novel mechanism: the recombinase RecA from E. coli, and phage T4 gene 32 single-stranded DNA–binding protein (gp32). Working in tandem, gp32 smooths and detangles the loose DNA ends, and RecA then guides each strand to its correct match.

Initial screening and secondary experiments identified RecA-Assisted Pair-and-Finish HiFi Assembly (RAPF) and Transformation 7 (T7) as the top enzymatic and transformation protocols, respectively. Both RAPF assembly and T7 transformation independently improved cloning efficiency relative to the base HiFi reaction cloning protocol, 2.6-fold and 36-fold respectively; and combined to provide an additive improvement in performance of 79-fold. All clones were confirmed by sequencing. (Error bars: SD of n=3 independent validation experiments).

While early, these results are encouraging. The improvements are specific to our particular cloning set up used in our model system, and still require human scientists to set up and run the protocols. Even so, these experiments show that AI systems can meaningfully assist real laboratory work and may accelerate human scientists in the future.

Notably, the AI-lab loop was run with fixed prompting and no human intervention. This scaffolding helped reveal the model’s capacity to propose genuinely novel protocol changes independent of human guidance, but it also locked the system into exploration and limited its ability to maximize the performance of newly discovered ideas. A better dynamic balance between exploration and exploitation would likely yield larger gains, as both the enzymatic and transformation improvements have substantial room for refinement. We expect advances in planning and task-horizon reasoning to improve the ability of simple fixed prompts to support both discovery and subsequent optimization.

An evolutionary framework for optimizing real-world protocols

The Gibson assembly⁠ reaction has been a primary cloning method since its invention in 2009, with widespread adoption across molecular biology. Gibson assembly lets molecular biologists “glue” pieces of DNA together by briefly melting their ends so matching sequences can be sealed into a single molecule. One major appeal of Gibson assembly is its simplicity: everything happens in a single tube at one temperature. Those constraints naturally leave room for improvement. In addition, the following properties make it well-suited to evaluating AI models’ abilities to improve wet lab techniques:

• Well-defined with controlled components, unlike a cell-based system
• Has a clear optimization function: transformable circularized DNA made from a fixed amount of linear DNA inputs
• Relatively fast experimental cycles (1-2 days)
• High-dimensional design space that requires mechanistic reasoning to improve: optimal buffers, reagents, and temperatures are all interdependent

We used HiFi assembly⁠, a proprietary enzyme system developed by New England Biolabs and based on Gibson assembly, as an optimization starting point. We explored whether an AI could innovate and learn from experimental feedback once the single-step and isothermal constraints were removed, and thereby identify protocol improvements in this scenario.Specifically, we performed a two-piece cloning reaction using a gene for green fluorescent protein (GFP) and the widely used pUC19 plasmid, a standard DNA “vehicle” used to carry genes into bacteria so they can be copied. The goal was to increase the number of successful colonies.

We optimized the cloning reaction by…