README.md

metadata

tags:
  - rna
  - sirna
  - bioinformatics
  - drug-discovery
  - pytorch
  - onnx
  - webgpu
  - research
library_name: pytorch

KDMSi: Base3 and Base0 siRNA design models

KDMSi is a staged computational workflow for prioritizing unmodified siRNA target sites and ranking chemically modified siRNA duplexes. This repository contains the released KDMSi-Base3 and KDMSi-Base0 checkpoints, browser-compatible ONNX exports, the RNA-FM backbone used to construct residue representations, and the preprocessing assets required by the KDMSi web application.

• Interactive Space: leighv318/KDMSi-webapp
• Source code: leighv318/KDMSi

Model components

KDMSi-Base3

Base3_cross_access_v2_src_no_se ranks unmodified siRNA candidates. It combines guide and local target-mRNA RNA-FM representations, recurrent shared-weight Transformer encoders, guide-to-mRNA cross-attention, handcrafted sequence descriptors, thermodynamic/accessibility features, and source information.

The standard inference release is a ten-fold ensemble:

• PyTorch: base3/pytorch/0-inter/ through base3/pytorch/9-inter/
• ONNX: base3/onnx/0-inter.onnx through base3/onnx/9-inter.onnx

The base3/pytorch/T/ and base3/pytorch/R/ directories contain the Takayuki and Reynolds source-held-out development checkpoints. They are evaluation artifacts and are not part of the standard ten-fold inference ensemble.

KDMSi-Base0

Base0_mrna_full_no_se ranks chemically modified antisense/sense duplexes using position-resolved nucleotide and chemical representations, local mRNA context, concentration, cell type, and biophysical descriptors.

The standard inference release is a ten-fold ensemble:

• PyTorch: base0/pytorch/0-inter/ through base0/pytorch/9-inter/
• ONNX: base0/onnx/0-inter.onnx through base0/onnx/9-inter.onnx

Gene-named directories under base0/pytorch/ contain leave-one-gene-out development checkpoints. They are provided for reproducibility and are not used by the standard web ensemble.

Shared assets

• shared/rnafm/RNA-FM_pretrained.pth — RNA-FM PyTorch weights used to produce 640-dimensional residue representations.
• shared/rnafm/rnafm_t12.onnx — browser-compatible RNA-FM export used by the KDMSi Space.
• shared/preprocessing/base3_pssm.json — fold-specific Base3 PSSM data.
• shared/preprocessing/base0_mod_features.json — Base0 chemical representation lookup data.
• shared/preprocessing/base0_fold_context.json — fold-specific Base0 concentration and categorical context.
• shared/preprocessing/mod_distributions.json — empirical position-specific modification distributions used by the candidate sampler.
• shared/runtime/vrna.wasm — ViennaRNA WebAssembly runtime used to calculate browser-side structure and thermodynamic features.
• manifest.json — machine-readable release layout.
• checksums.sha256 — SHA-256 integrity hashes for every released file.

RNA-FM was developed by the ml4bio/RNA-FM authors. Its license is reproduced at shared/rnafm/RNA-FM_LICENSE.txt. Please cite the original RNA-FM publication when using these representations.

Download

Download the complete release with huggingface_hub:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="leighv318/KDMSi",
    repo_type="model",
)
print(local_dir)

To download only the browser inference assets:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="leighv318/KDMSi",
    allow_patterns=[
        "base3/onnx/*",
        "base0/onnx/*",
        "shared/rnafm/rnafm_t12.onnx",
        "shared/preprocessing/*",
        "shared/runtime/vrna.wasm",
        "manifest.json",
    ],
)

The checkpoints use custom KDMSi model classes and feature construction; they are not directly loadable with transformers.AutoModel. Use the KDMSi source repository or the browser application for complete preprocessing and inference.

Intended use

The models are intended for research-use prioritization of siRNA candidates before experimental validation. Base3 is used for unmodified target-site ranking; Base0 is used for ranking chemically modified duplexes under the represented molecular and assay context.

Limitations

• Predicted scores do not replace experimental validation.
• Base0 supports only the chemical-modification vocabulary represented by its released encoder and training data.
• The standard Base0 score estimates inhibition under modeled sequence, chemistry, transcript, concentration, and cell-type conditions. It does not predict delivery, pharmacokinetics, toxicity, or clinical efficacy.
• Optional off-target annotation is implemented in the KDMSi workflow rather than these checkpoint files. It is sequence based and does not model modification-dependent changes in off-target activity.
• Base3 source-held-out results using adaptive batch normalization should be interpreted as transductive source-adaptation estimates rather than strict frozen zero-shot estimates.
• Sample-level cross-validation is a development/interpolation benchmark and should not be conflated with leave-one-gene-out or group-disjoint external evaluation.

Release and licensing note

The KDMSi project license has not yet been finalized. Public availability of the files should not be interpreted as permission for clinical use. The bundled RNA-FM license applies to the RNA-FM component and is included separately. Contact the KDMSi authors regarding reuse outside research evaluation.

Citation

The KDMSi manuscript citation will be added after publication. Until then, please cite the repository and the original RNA-FM work when using these files.