NaviKinase 1.0

Model details

Training data

• Scope: UniProtKB entries with EC 2.7.- (phosphotransferase superfamily), length 30-2000 residues, non-fragment.
• Deduplicated at the UniRef90 cluster level (one representative sequence per cluster) to prevent overfitting to near-identical bacterial strain variants; train/val/test split at the cluster level to keep held-out evaluation meaningful.
• 2,450,504 training sequences, 1,320,176,103 training tokens.
• Each example is packed as <BOS> <EC-tag> residues <EOS>, where the EC-tag conditions generation on one of 14 EC 2.7.- sub-subclasses (e.g. <EC_2.7.11> for protein-serine/threonine kinases) or <EC_UNKNOWN> for sequences without a recognized sub-subclass (~3.3% of the corpus).

Training

• AdamW (betas 0.9/0.95, weight decay 0.1), cosine LR schedule with warmup, peak LR 3e-4.
• Effective batch size 64 sequences/step (batch 4 × grad-accum 16), 1024-token context.
• Ran the full 40,289-step ceiling (2 epochs) to completion — no early stopping triggered (patience=5 eval intervals, val loss kept improving every eval).
• Trained unattended over several days on a MacBook, with automatic checkpointing, battery-aware pause/resume, and crash recovery via launchd.

Evaluation

Motif presence (qualitative, not a hard benchmark)

Usage

• fetch_uniprot_by_accession(accession) — real protein sequence by exact accession, unambiguous.
• fetch_uniprot_candidates(query, n) — best-effort free-text name/keyword search, returns up to n real candidates (accession, name, organism, sequence) for disambiguation — verified against the live API to not reliably rank the intended protein first for ambiguous multi-word queries, which is why callers should show candidates rather than assume the top hit.
• fetch_pubmed_abstracts(query, n) — real citations/abstracts for a keyword search. Informational only — displayed as plain text for you to read; NaviKinase cannot process or reason about this text, it is never passed into the model.

Files

• model.safetensors — model weights (fp32)
• model.py — standalone MLX architecture definition
• tokenizer.py — frozen tokenizer/vocab for this specific checkpoint
• config.json — architecture hyperparameters
• generate.py — generation / sequence-continuation script
• score.py — plausibility scoring for a real sequence
• motifs.py — regex catalytic-motif scan (P-loop, HRD, DFG)
• chat.py — plain-English pattern-matching front end over all the tools here
• nlu.py — shared request-parsing/clarification helpers (used by chat.py and research.py)
• research.py — multi-step autonomous "research protein X" pipeline
• lookup.py — live UniProt sequence fetch + PubMed citation search (needs internet)
• structure.py — real 3D structure prediction via ESMFold (needs internet; a different model, not NaviKinase)
• fold.py — standalone CLI for structure.py

Important: use the bundled tokenizer.py, not a shared/updated one

Responsible use / dual-use disclosure

• Treat it as an unvalidated hypothesis, not a confirmed real protein — see Evaluation above for exactly what has and hasn't been measured.
• Route synthesis through a DNA synthesis provider that performs standard biosecurity screening (most reputable providers already do this as a matter of course).
• Independently review function and safety yourself; nothing in this repo screens for toxicity, pathogenicity, or hazard.

Limitations

• Scope is EC 2.7.- specifically — it has no exposure to other transferase classes, non-enzymatic proteins, or non-protein biomolecules.
• Character-level, single-sequence model: no structural, multiple-sequence- alignment, or homology information is used, only raw sequence statistics.
• ~3.3% of training data has no recognized EC sub-subclass and was trained under a shared <EC_UNKNOWN> tag.
• Motif-presence checks are a cheap directional signal, not a validated functional or structural correctness benchmark.
• chat.py is keyword/regex pattern matching, not a language model — it only recognizes the request styles listed in its docstring, and says so explicitly rather than guessing when a request doesn't match. It does not give NaviKinase any actual English understanding.
• score.py --classify is an improvised proxy (comparing loss under each EC tag) — the model was never trained as a classifier, so treat its output as a rough signal, not a validated prediction.
• lookup.py's UniProt name/keyword search (as opposed to exact accession lookup) is best-effort free-text ranking, not guaranteed to return the specific protein you meant for ambiguous or multi-word queries — always check the accession it reports. fetch_pubmed_abstracts results are shown to you verbatim and are never interpreted by the model. Both require internet access, unlike the rest of this release, which is fully offline.
• research.py's pipeline runs a fixed sequence of steps (fetch, score, classify, motif scan, literature search) — it is not a dynamic planner that decides what to do based on reasoning about your request. The classify step inherits the same "experimental, unvalidated" caveat as score.py --classify above.
• structure.py/fold.py call ESMFold, a separate third-party model — not NaviKinase, and not covered by this repo's evaluation numbers above. It's a free public service with no uptime guarantee and no length limit enforced here; large sequences may be slow or fail independent of NaviKinase itself. Requires internet access.

License