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

268

Benchmarks

Active tool targets

152

Datasets

Methods

15.8×

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.

Baseline and gates

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

Iterate and verify

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

Release and package

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

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.
Download PDF ↓

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

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

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

ProDy

7b0u · 1 threads

175.5×

20.17 min → 6.90 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}
}