· Evaluation report
NaviKinase 1.0: evaluation of a domain-specific protein language model for the phosphotransferase superfamily
How NaviKinase performs on sequences it never saw in training, and how it compares with ESM-2 and ProtGPT2.
Products / Language Models
EC 2.7.- Phosphotransferase Superfamily
The protein language model
NaviKinase 1.0 is a 149.6M-parameter decoder-only transformer trained on Apple Silicon via MLX. It speaks the language of kinases: 2.45M UniProtKB sequences, one enzyme superfamily.
149.6M
PARAMETERS
2.45M
TRAINING SEQUENCES
4.32
HELD-OUT TEST PERPLEXITY
95.6%
EC CLASS IDENTIFIED (HELD-OUT)
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Architecture
| Architecture | Decoder-only transformer |
| Normalisation | Pre-norm RMSNorm |
| Position encoding | RoPE |
| Layers | 18 |
| d_model | 832 |
| Attention heads | 13 (head dim 64) |
| FFN dimension | 3,328 |
| Context length | 1,024 tokens |
| Precision | fp32 |
| Framework | MLX |
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Research
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What it can do
NaviKinase doesn’t speak English — there’s no chat template, no instruction-following. It speaks amino acid sequences. Everything around it is tooling that makes that useful.
Sequence generation
Generate from scratch, condition on one of 14 EC 2.7.- sub-subclasses, or continue a real sequence you provide.
Plausibility scoring
Teacher-forced loss over a real sequence — a plausibility signal for how “kinase-like” it reads to the model.
Plain-English front end
A pattern-matching router (not an LLM) that maps natural requests onto the tools — and asks rather than guesses when it matters.
Live lookups
Real UniProt accession fetches and PubMed citation search — the only parts of this release that need internet access.
Research pipeline
“Research protein X” chains fetch → score → classify → motif scan → literature search into one synthesised report.
Motif scanning
Regex checks for canonical catalytic motifs — P-loop, HRD, DFG — as a cheap directional signal, not a benchmark.
3D structure
Real structure prediction via ESMFold — a genuinely different model, called on NaviKinase’s output or any fetched sequence.
Non-synthesis notice
Every fresh generation carries an automatic notice: treat output as an unvalidated hypothesis, not a confirmed protein.
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How it was built
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3.1
Data at the cluster level
UniProtKB entries with EC 2.7.-, 30–2000 residues, non-fragment — deduplicated at the UniRef90 cluster level so near-identical bacterial strain variants can’t dominate training, with train/val/test split kept at the same cluster level.
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3.2
EC-tag conditioning
Each example is packed as
<BOS> <EC-tag> residues <EOS>, conditioning generation on one of 14 EC 2.7.- sub-subclasses, or<EC_UNKNOWN>for the ~3.3% without a recognised one. -
3.3
AdamW, cosine schedule
Betas 0.9/0.95, weight decay 0.1, peak LR 3e-4, effective batch size 64 (batch 4 × grad-accum 16) over a 1,024-token context.
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3.4
Built to survive a laptop
Automatic checkpointing, power-aware pause/resume, and crash recovery via
launchd— designed for unattended runs on constrained hardware without a dedicated server environment.
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Scope, limits & responsible use
Scope is EC 2.7.- only: no exposure to other transferase classes, non-enzymatic proteins, or non-protein biomolecules.
Character-level, single-sequence model — no structural, MSA, or homology information, only raw sequence statistics.
Motif-presence checks are a cheap directional signal, not a validated structural or functional benchmark.
chat.py is keyword/regex pattern matching, not a language model, and says so rather than guessing on an unrecognised request.score.py --classify is an improvised proxy — the model was never trained as a classifier.UniProt name search is best-effort free-text ranking, not guaranteed to surface the intended protein first.
Dual-use disclosure — this repo has no content moderation, toxin/hazard blocklist, or biosecurity screening, and has not been reviewed by a biosecurity expert. The realistic risk is limited by scope — EC 2.7.- kinases are among the most common, well-characterised enzyme families, present in nearly all organisms and not associated with toxicity — but that narrowness came from data and model-size decisions, not an engineered safety feature. Before any wet-lab use: route synthesis through a provider that performs standard biosecurity screening, and independently review function and safety yourself.
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Licence
CC BY-NC 4.0
Free for non-commercial use
Use, share, and adapt NaviKinase 1.0 freely for non-commercial purposes with attribution. Weights, code, and tokeniser are covered.
Commercial licensing
Building a product on this?
Any use intended to generate revenue requires prior written permission. Reach out and we’ll work out terms.
jaydev.bhatt@navitratech.comTrained for a laptop. Ready for yours.
Pull the weights, run it locally with MLX, and see what a purpose-built, single-family protein model can do.