ArticleNature chemical engineering2024
Self-driving laboratories to autonomously navigate the protein fitness landscape.
Article in Nature chemical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
85 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- Automated synthetic cell-based screening for designed proteins with emergent functions.Nature communications · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Microbial cell-free protein synthesis and its progression toward industrial use.Microbiology (Reading, England) · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Large Language Model and Pediatric Neurosurgery - A Neurosurgeon's Perspective on the Artificial Nervous System.Journal of Korean Neurosurgical Society · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- Technology integration in intensive care medicine through clinical engineering and convergent biotechnology.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026Article
- Open-source robotic chip-to-plate interface for high-throughput microfluidic generation of materials libraries.bioRxiv : the preprint server for biology · 2026Article
- SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials.Journal of the American Chemical Society · 2026Article
- Harnessing Nature's Algorithm: From Test Tubes to Autonomous In Vivo Evolution.Biotechnology journal · 2026Review
- Sequence Display enables large-scale sequence-activity datasets for rapid protein evolution.Nature biotechnology · 2026Article
- Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- What are the limits to biomedical research acceleration through general-purpose AI?Scientific reports · 2026Review
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence-function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent's understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.