cdskit localize targeting5 v1

cdskit localize targeting5-v1

targeting5-v1 is the pretrained model used by:

cdskit localize --seq_file proteins.faa --seq_type protein --model targeting5 --report localize.tsv

It predicts the TargetP-compatible five-class targeting-peptide labels:

TargetP label	cdskit label
Other	noTP
SP	SP
MT	mTP
CH	cTP
TH	lTP

The model file is cdskit-localize-targeting5-v1.pt. When it is not already cached, cdskit localize --model targeting5 downloads it from the cdskit GitHub Release and verifies its SHA-256 checksum before loading it.

Dependencies

The pretrained model runs on CPU, but it uses optional ML dependencies at runtime:

• torch
• scikit-learn

Install cdskit with the ml extra, or install these packages separately.

Model cache

Downloaded models are cached under $CDSKIT_MODEL_DIR when set. Otherwise, cdskit uses the standard user cache directory.

Training data

TargetP-2.0 and the cdskit targeting5-v1 model use the same five target labels, but they do not use identical training recipes. cdskit does not use TargetP executable code, trained weights, or fitted parameters.

Data source	Rows	Class counts	Role
TargetP-2.0 public table, targetp2_benchmark.tsv	13,005	noTP 9,537; SP 2,697; mTP 499; cTP 227; lTP 45	canonical TargetP-compatible supervised labels for base training
Strict external table, targetp2_external_torch_strict_cleanholdout_seed1seed2_plus_thylum_h128_e6_external.tsv	13,983	noTP 4,684; SP 3,986; mTP 2,914; cTP 1,872; lTP 527	external augmentation for base PyTorch models
Reranker development holdout	20,000	4,000 per class	trains the multiclass HGB reranker and class thresholds
mTP/noTP specialist train	16,000	mTP 8,000; noTP 8,000	trains the binary mTP/noTP specialist
mTP/noTP specialist validation	2,000	mTP 1,000; noTP 1,000	selects the specialist threshold, currently 0.41

Model structure

Component	cdskit targeting5-v1
Base predictors	Two cdskit-trained TargetP2-style PyTorch models (sqrt and logcw)
Blend	Classwise alpha 0.4
Thresholds	noTP 1.0, SP 0.65, mTP 1.0, cTP 0.8, lTP 1.0
Reranker	scikit-learn HGB multiclass reranker
Specialist	scikit-learn HGB mTP/noTP specialist
Inference device	CPU

Performance snapshot

These values are published-model checks, not an official TargetP-2.0 server rerun.

Model / evaluation	Rows	Macro F1	Accuracy	noTP F1	SP F1	mTP F1	cTP F1	lTP F1
TargetP-2.0 paper Table 1	-	0.890	-	0.980	0.980	0.860	0.880	0.750
cdskit targeting5-v1, seed2 strict external holdout	1,801	0.891	0.895	0.773	0.921	0.851	0.950	0.957
cdskit targeting5-v1, fresh unused balanced holdout	1,000	0.907	0.909	0.796	0.915	0.861	0.976	0.990

The model is TargetP-2.0-class for this five-class targeting-peptide task. It should not be treated as a general DeepLoc replacement.

Sources

• TargetP-2.0 data page
• TargetP-2.0 source repository
• cdskit targeting5-v1 release