Research preview · bioinformatics performance layer

Make scientific software faster.

AutoZyme is the optimization layer for research code: autonomous search finds speedups inside widely used scientific tools, shipped as safe, drop-in patches — same API, same outputs, no workflow changes.

See the benchmarks → Submit an optimization Request a package
Preprint coming soon · arXiv:TBD First public releases: SeuratTurbo · R / ScanpyTurbo · Python · 59 accelerated tools across 9 scientific domains

By the numbers

Aggregated across all tracked benchmarks
277
Benchmarks
59
Active tool targets
161
Datasets
58
Methods
16.9×
Median speedup
5,633×
Peak speedup

How AutoZyme finds and compounds speedups

Agent discovery and community requests converge into one verification pipeline
Agent-discovered

AutoZyme searches for bottlenecks

The AutoZyme agent scans public bioinformatics and scientific-computing ecosystems for slow, memory-heavy, or widely used methods worth optimizing.

Community-requested

Researchers nominate pain points

Users request packages that are too slow, hit OOM, or waste repeated analysis time. Votes and reproducibility help prioritize the queue.

01

Baseline and gates

We freeze representative inputs, upstream baselines, output concordance metrics, and acceptance thresholds.

02

Iterate and verify

AutoZyme generates candidate changes, benchmarks them, rejects divergent outputs, and keeps only reproducible speedups.

03

Release and package

Accepted optimizations are published as drop-in AutoZyme packages or upstream-ready patches with reproducible benchmark evidence.

04

Return to Lab

The optimized result becomes the new public baseline in AutoZyme Lab, where contributors can try to push it further.

Lab is not only a submission portal. It is the public continuation point: every verified release can become a new frozen challenge baseline for the community to improve again.

Benchmarks at a glance

Released public subset; broader bioinformatics targets are staged as they pass gates
AutoZyme benchmark figure: per-method speedup and concordance for Seurat (panel A) and Scanpy (panel B), across four dataset sizes
Figure 1 Per-method wall-clock speedup (left), before/after timings, peak memory, and output concordance, for Seurat (A) and Scanpy (B) at 4 threads on an AMD Ryzen 9 7950X.
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Top speedups

Best wall-clock ratio per method
AutoDock-Vina
AutoDock Vina
pde6d_5x72_p69_exh1536 · 1 threads
5,633×
24.88 min → 265 ms
Scanpy
sc_rank_genes
heart_adult · 32 threads
4,363×
21.81 min → 300 ms
clusterProfiler
compareCluster
tms_ss2 · 1 threads
1,219×
39.92 min → 1.97 s
nichenetr
NicheNet
tms_spleen_BvsT_x6 · 1 threads
1,061×
7.76 min → 439 ms
xclim
xclim
synth_500x500x30y · 1 threads
934.6×
2.51 min → 161 ms
cellphonedb
call
glaucoma_cpdb_k22_c25 · 1 threads
889.6×
51.94 min → 3.50 s
mdanalysis
MDAnalysis.analysis.rms.RMSD.run
adk_perturbed_cycle_large · 1 threads
306.4×
10.01 min → 1.96 s
seurat
FindAllMarkers
pbmc68k_full · 1 threads
218.8×
2.73 min → 750 ms
CellChat
computeCommunProb
heart_adult_30k · 1 threads
180.7×
5.80 min → 1.93 s

Install

Drop-in — no API changes to your existing pipelines
R · SeuratTurbo Seurat v5.x 
# Install from GitHub (CRAN release coming)
remotes::install_github("ElliotXie/seurat-turbo")

library(Seurat)
library(SeuratTurbo)   # activates patches

# Use Seurat exactly as you normally would —
# NormalizeData / RunPCA / FindClusters / etc.
# are transparently accelerated.
Requires R ≥ 4.0, Seurat ≥ 5.0
Python · ScanpyTurbo Scanpy v1.11.x 
# Install from GitHub (PyPI release coming)
pip install git+https://github.com/ElliotXie/scanpy-turbo.git

import scanpy as sc
import scanpy_turbo   # activates patches

# Use Scanpy exactly as you normally would —
# pp.normalize_total / tl.leiden / etc.
# are transparently accelerated.
Requires Python ≥ 3.10, Scanpy ≥ 1.11

How AutoZyme works

Under the hood, AutoZyme runs an autonomous research loop: candidate optimizations are generated, benchmarked against the upstream baseline on real datasets, and filtered on both speed and output concordance. The same framework applies beyond the first single-cell releases: any bioinformatics or scientific-computing method with slow runtime, out-of-memory failures, or repeated human waiting time can become an AutoZyme target.

You can nominate packages on the Suggest & Vote page or contribute directly through AutoZyme Lab. The long-term goal is a shared optimization layer for science: community requests, frozen benchmark gates, hidden validation, and credited speedups that flow back to everyone.

How to cite

@misc{autozyme2026,
  title  = {AutoZyme: Autonomous-Research-Driven Speedups for Scientific Toolkits},
  author = {The AutoZyme Team},
  year   = {2026},
  note   = {Manuscript in preparation}
}