SLp

SLp models molecular state and the consequences of genetic interventions. Synthetic-lethality prediction is a downstream application of the learned world.

Release	Artifact path	Description
SLp-1.1 r1	checkpoints/v1.1/SLp-1.1-r1/	15.12M-parameter molecular and functional world; RNA/protein generation, continuous fitness decoding and ten downstream SL decoders
SLp-1	checkpoints/v1/SLp-1/	Frozen historical proof of concept

Source and usage · Model card · Prepared data

SLp-1.1 r1 includes actual weights, inference source, normalizers, biological contexts, a descriptor registry and downstream decoder weights. Use the revision-pinned download command in the source repository; inference does not require the training corpus. The original SLp code and weights are MIT. Bundled biological data retain their source terms; see the release notices. Historical artifacts and their terms are preserved.

On ten official MuSL CV3 splits, SLp-1.1's downstream decoder has mean AUROC 0.787830 and AP 0.780602. Its matched untrained functional component reaches 0.734081/0.729673, while direct descriptors reach 0.798579/0.802964. The molecular and functional worlds are trained on quantitative observations; human SL labels train only the separate fold-local decoder. These retrospective results are research evidence, not a prospective SOTA claim or clinical validation.

The pretraining mix combines human CRISPRi/CRISPRa RNA, paired RNA/protein, native yeast RNA populations, human single-gene fitness and yeast double-mutant fitness. See the model card for exact populations, units, sampling proportions, exclusions and species-specific results.