Back

An AI-powered cloud biofoundry for autonomous biological research

Chen, J.; Volk, M.; Shafaei, S.; Tan, S.-I.; Upadhyay, V.; Singh, N.; Zhong, X.; Arneson, K.; He, W.; Wang, C.; Lu, J.; Sharma, A.; McLaughlin, J.; Berry, M.; Harmon, S.; Reyes, S.; Heintz, G.; Zhao, H.

2026-10-01 synthetic biology
10.64898/2026.09.30.750244 bioRxiv
Show abstract

Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
ACS Synthetic Biology
● 287 papers in training set
Top 0.2%
18.5%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 16%
11.9%
Similar papers in this journal
3
Cell Systems
○ 201 papers in training set
Top 0.3%
9.8%
Similar papers in this journal
4
Nature Methods
○ 385 papers in training set
Top 2%
6.3%
Similar papers in this journal
5
Molecular Systems Biology
○ 162 papers in training set
Top 0.5%
4.0%
Similar papers in this journal
50% of probability mass above
6
Trends in Biotechnology
○ 12 papers in training set
Top 0.1%
3.2%
7
iScience
○ 1154 papers in training set
Top 8%
2.8%
8
PLOS ONE
● 5266 papers in training set
Top 38%
2.7%
9
Scientific Reports
○ 3612 papers in training set
Top 46%
2.1%
10
Journal of Molecular Biology
○ 232 papers in training set
Top 1%
2.1%
11
Nucleic Acids Research
◐ 1281 papers in training set
Top 8%
2.1%
12
Bioinformatics
◐ 1204 papers in training set
Top 6%
1.9%
13
Nature Biotechnology
○ 172 papers in training set
Top 2%
1.9%
14
Cell
○ 431 papers in training set
Top 6%
1.9%
15
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.7%
16
npj Systems Biology and Applications
○ 125 papers in training set
Top 1.0%
1.7%
17
Synthetic Biology
◐ 24 papers in training set
Top 0.2%
1.5%
18
eLife
● 5828 papers in training set
Top 53%
1.5%
19
Nature
○ 645 papers in training set
Top 8%
1.4%
20
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 4%
1.3%
21
Advanced Intelligent Systems
○ 11 papers in training set
Top 0.2%
1.1%
22
Protein Science
○ 246 papers in training set
Top 3%
1.1%
23
Science Advances
● 1243 papers in training set
Top 29%
0.9%
24
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 41%
0.8%
25
Neuron
○ 337 papers in training set
Top 5%
0.8%
26
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 2%
0.8%
27
npj Antimicrobials and Resistance
○ 11 papers in training set
Top 0.2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.