There’s probably nothing more inspiring than using AI in the field that brings the greatest benefit to humanity and the world as a whole ‒ science and research. And right now, we’re seeing a sharp leap in the rise of new AI systems acting as co-scientists.

No, this doesn’t reduce the value of scientists, engineers, or researchers ‒ these systems help people generate and analyze results much faster, accelerating progress in biology, chemistry, physics, medicine, and beyond. Massive amounts of tedious analysis, hypothesis testing, and experimentation that used to take many years, can now be done in days, sometimes even hours.

So today, we’ll look at some of the most interesting AI co-scientist systems.

TL;DR: Use DeepMind’s Co-Scientist and Robin for biology and drug discovery, AxiomProver and AI Co-Mathematician for theorem proving and mathematical research, ERA and AI CFD Scientist for autonomous scientific simulations, and systems like The AI Scientist or AutoResearchClaw for fully automated end-to-end research workflows.

Some of them are biology co-scientists discovering drugs and engineering proteins. Some are AI mathematicians solving proofs and conjectures. Others generate scientific simulations and experiments, and there are also systems that automate the entire research pipeline, even including paper writing. Further, you’ll also find 6 open-source co-scientists.

This is one of the defining AI systems of 2026 that can reduce large-scale biological data analysis from months to days. Built on Gemini as a multi-agent research architecture, it generates, debates, ranks, and evolves scientific hypotheses through iterative “idea tournaments” inspired by AlphaGo. The system is already being used for fibrosis, ALS, antimicrobial resistance, cellular aging, infectious diseases, and plant immunity research ‒ including identifying a fibrosis drug candidate that blocked 91% of a scarring-linked response in lab tests.

Co-Scientist is the core research engine behind Google’s broader Gemini for Science initiative: the “Hypothesis Generation” tool is explicitly built on Co-Scientist. Google positions it as part of a larger ecosystem alongside AlphaEvolve (for computational experiments) and NotebookLM-based Literature Insights.

OpenAI’s new reasoning model (it’s name is not mentioned) has just solved a math problem that had stumped researchers since 1946 ‒ one of Paul Erdős most famous geometry puzzles: if you place n points on a plane, how many pairs can sit exactly one unit apart? For nearly 80 years, mathematicians believed square-grid-like patterns were basically optimal.

The AI proved that they weren’t. A general-purpose reasoning model (not a math-specialized system) discovered a completely new construction that creates far more unit-distance pairs than anyone expected. It used deep ideas from algebraic number theory that experts never thought connected to this geometry problem. Mathematicians verified the proof and called it a milestone for AI-driven mathematics.

This multi-agent system autonomously generates and experimentally validates a therapeutic hypothesis for a real lab workflow: reads papers, generates hypotheses, proposes experiments, analyzes lab data, and iterates on results in a loop.

Researchers tested it on dry macular degeneration ‒ a major cause of blindness. Robin proposed that improving retinal cell “cleanup” (phagocytosis) could help treat the disease, then identified ripasudil (a glaucoma drug) as a candidate. Lab tests confirmed the real effect of ripasudil.

Robin also analyzed RNA-seq data itself and discovered a possible new target, ABCA1.

AxiomProver is an AI mathematician that writes fully machine-verified mathematical proofs in Lean. In 2026, it autonomously solved all 12 problems of the Putnam exam, one of the hardest undergraduate math competitions in the world, with 8 solved within the official contest time. It handles calculus, combinatorics, geometry, and number theory problems, and sometimes discovered proof strategies humans didn’t expect, including geometric arguments and brute-force formal reasoning.

Here is another research partner from Google DeepMind but for mathematicians. It uses multiple AI agents based on Gemini that work in parallel, almost like a research team in a collaborated workspace. One agent might search literature, another explores examples, while others try proving or disproving ideas. Researchers can guide the process interactively.

Mathematicians used it to solve open problems, discover new research directions, and find overlooked papers. It also scored a record 48% on one of the hardest math benchmark ‒ FrontierMath Tier 4.

This AI system by a group of researchers from top universities like MIT, Harvard, McGill University and Google researchers generates scientific simulation code for complex research problems, helping scientists build computational experiments and large-scale simulations much faster. ERA continuously writes, tests, scores, and improves scientific software through trial and error using AlphaZero-style tree search. It can combine ideas from papers, textbooks, and existing methods to invent new hybrid approaches.

The most notable results:

Watch this video to get a full picture of how AI is transforming science and why it feels so important now →

This AI system solves extremely hard math optimization problems, where there are trillions of possible solutions. It works in a loop: an AI model learns patterns from good solutions, generates new candidate solutions, and then a local search algorithm fixes and improves them. Over time, the system gets better at proposing promising candidates.

The team has already used it on difficult combinatorics problems like square-free graphs and isosceles-free point sets. In one case, Axplorer found an optimal graph solution in 2.5 hours on a single GPU, using about 100× fewer attempts than earlier methods.

This multimodal generative model co-designs entirely new proteins and enzymes from scratch. You give it a target molecule or desired chemistry, and it invents both the protein sequence and the 3D structure simultaneously.

DISCO’s concept may sound too similar to DeepMind’s AlphaFold, but AlphaFold’s idea is different: it predicts how an existing protein folds into a 3D structure. AlphaFold is mainly a prediction modeling system, while DISCO is a generative design system.

CuspAI introduced kUPS, an open-source molecular simulation engine for AI-driven chemistry and materials science. kUPS unifies molecular dynamics, Monte Carlo simulations, geometry optimization, and ML-based force fields inside one GPU-native Python/JAX framework. It’s designed around composable “propagators” ‒ different simulation techniques and ML models can plug together easily. The system also supports batching thousands of simulations in parallel on GPUs. kUPS tool gives 49× faster simulations than standard chemistry tools.

This “AI scientist” is built specifically for computational fluid dynamics (CFD) ‒ the kind of simulations engineers use to study airflow, turbulence, jets, aerodynamics, combustion, and fluid motion. CFD is much harder than normal AI research automation, because a simulation can technically “work” while still being physically wrong. So the biggest breakthrough here is that the system includes a physics-aware verification loop: it renders flow simulations and uses a vision-language model (VLM) to check whether the physics looks realistic. If flow patterns are wrong, the AI rejects the run and fixes or reruns the simulation.

After 44 iterative experiments, AI CFD Scientist discovered a new correction for the Spalart–Allmaras turbulence model that improved wall-friction prediction accuracy by 7.89%.

We can’t not to mention Sakana AI’s AI Scientist, even though it was developed in 2024. It automates the full machine learning research workflow: generating ideas, searching literature, writing code, running experiments, analyzing results, and drafting complete research papers. One of its biggest milestones was producing the first fully AI-generated paper to pass a real human peer-review process at an ICLR workshop. AI Scientist-v2 also introduced agentic tree search, parallel experiment execution, and VLM-based figure checking to improve experiments and paper quality. The project also showed a clear “scaling law”: stronger foundation models consistently produced higher-quality scientific papers.

This AI co-scientist automates the full research loop: generating hypotheses, running experiments, fixing failed code, analyzing results, and writing papers. It uses multiple debating agents, can “self-heal” broken experiments, and stores lessons from past runs. It also verifies citations and checks that every reported metric comes from real experiment logs to reduce hallucinations and fake results.

AI co-scientists are autonomous or semi-autonomous AI systems designed to assist with scientific discovery. Unlike standard chatbots, they can generate hypotheses, analyze papers, run experiments, write code, evaluate results, and sometimes even produce research papers.

Some systems specialize in biology and drug discovery, others focus on mathematics, scientific simulations, physics, or materials science. Newer systems increasingly combine multiple agents into autonomous research teams.

Google DeepMind’s Co-Scientist, FutureHouse’s Robin, and DISCO are among the strongest biology-focused systems. They work on drug discovery, protein engineering, disease research, and therapeutic hypothesis generation.

AxiomProver, AI Co-Mathematician, Axplorer, and OpenAI’s reasoning systems focus on theorem proving, formal verification, optimization, and solving difficult mathematical problems.

Systems like ERA, kUPS, and AI CFD Scientist autonomously generate scientific simulation code, run computational experiments, optimize models, and verify results across fields like biology, fluid dynamics, chemistry, and forecasting.

Systems like The AI Scientist and AutoResearchClaw automate nearly the full research workflow: literature review, idea generation, experimentation, debugging, analysis, verification, and paper writing.

Yes. Projects like The AI Scientist, Axplorer, DISCO, kUPS, AI CFD Scientist, and AutoResearchClaw are open-source and allow researchers to inspect, modify, and extend their scientific workflows.

The rise of reasoning models, long-context memory, multi-agent systems, and tool-using AI agents has made it possible for AI systems to handle long-horizon scientific workflows that previously required large human research teams.

Further reading

If you're just getting started with ML and AI, check out our curated list of Top 10 GitHub repos for AI & ML practitioners— collections of courses, guides, and projects to build your foundations.

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