README.md

---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_format: pickle
model_file: MLR-model.pkl
widget:
- structuredData:
    CAS:
    - 696-71-9
    - 94-02-0
    - 15128-82-2
    CID:
    - 12766.0
    - 7170.0
    - 27057.0
    CanonicalSMILES:
    - canonical: OC1CCCCCCC1
      original: C1CCCC(CCC1)O
    - canonical: CCOC(=O)CC(=O)c1ccccc1
      original: CCOC(=O)CC(=O)C1=CC=CC=C1
    - canonical: O=[N+]([O-])c1ncccc1O
      original: C1=CC(=C(N=C1)[N+](=O)[O-])O
    Cor1-C420 Adduct (M+H):
    - no Adduct
    - no Adduct
    - no Adduct
    Cor1-C420 Depletion 24 h (%):
    - 1.0
    - 1.0
    - 1.0
    Cor1-C420 Dimer (%):
    - 2.0
    - 5.0
    - 4.0
    Cor1-C420 Kmax (1/mM/min):
    - 6.979399898264935e-06
    - 6.979399898264935e-06
    - 6.979399898264935e-06
    DPRA Cysteine depletion (%):
    - .nan
    - 11.2
    - .nan
    DPRA Lysine depletion (%):
    - .nan
    - 0.9
    - .nan
    InChI:
    - InChI=1S/C8H16O/c9-8-6-4-2-1-3-5-7-8/h8-9H,1-7H2
    - InChI=1S/C11H12O3/c1-2-14-11(13)8-10(12)9-6-4-3-5-7-9/h3-7H,2,8H2,1H3
    - InChI=1S/C5H4N2O3/c8-4-2-1-3-6-5(4)7(9)10/h1-3,8H
    InChIKey:
    - FHADSMKORVFYOS-UHFFFAOYSA-N
    - GKKZMYDNDDMXSE-UHFFFAOYSA-N
    - QBPDSKPWYWIHGA-UHFFFAOYSA-N
    IsomericSMILES:
    - canonical: OC1CCCCCCC1
      original: C1CCCC(CCC1)O
    - canonical: CCOC(=O)CC(=O)c1ccccc1
      original: CCOC(=O)CC(=O)C1=CC=CC=C1
    - canonical: O=[N+]([O-])c1ncccc1O
      original: C1=CC(=C(N=C1)[N+](=O)[O-])O
    KeratinoSens EC1.5 (uM):
    - 249.6822169
    - 62.9764329
    - 4000.0
    KeratinoSens EC3 (uM):
    - 4000.0
    - 689.0
    - 4000.0
    KeratinoSens IC50 (uM):
    - 4000.0
    - 4000.0
    - 4000.0
    KeratinoSens Imax:
    - 2.830997136
    - 3.299878249
    - 1.036847118
    KeratinoSens Log EC1.5 (uM):
    - 2.3973876117256947
    - 1.7991780577657597
    - 3.6020599913279625
    KeratinoSens Log IC50 (uM):
    - 3.6020599913279625
    - 3.6020599913279625
    - 3.6020599913279625
    LLNA EC3 (%):
    - 100.0
    - 100.0
    - 100.0
    LLNA Log EC3 (%):
    - 2.0
    - 2.0
    - 2.0
    MW:
    - 128.21
    - 192.21
    - 140.1
    OPERA Boiling point (°C):
    - 186.863
    - 276.068
    - 323.069
    OPERA Henry constant (atm/m3):
    - 7.84426e-06
    - 5.86618e-07
    - 9.47507e-08
    OPERA Log D at pH 5.5:
    - 2.36
    - 1.87
    - -0.01
    OPERA Log D at pH 7.4:
    - 2.36
    - 1.87
    - -1.69
    OPERA Melting point (°C):
    - 25.1423
    - 49.3271
    - 128.292
    OPERA Octanol-air partition coefficient Log Koa:
    - 6.08747
    - 6.56126
    - 6.36287
    OPERA Octanol-water partition coefficient LogP:
    - 2.3597
    - 1.86704
    - 0.398541
    OPERA Vapour pressure (mm Hg):
    - 0.0839894
    - 0.000406705
    - 0.00472604
    OPERA Water solubility (mol/L):
    - 0.0510404
    - 0.01476
    - 0.0416421
    OPERA pKaa:
    - 10.68
    - .nan
    - 5.31
    OPERA pKab:
    - .nan
    - .nan
    - .nan
    SMILES:
    - canonical: OC1CCCCCCC1
      original: OC1CCCCCCC1
    - canonical: CCOC(=O)CC(=O)c1ccccc1
      original: CCOC(=O)CC(=O)c1ccccc1
    - canonical: O=[N+]([O-])c1ncccc1O
      original: OC1=CC=CN=C1[N+]([O-])=O
    TIMES Log Vapour pressure (Pa):
    - 0.8564932564458658
    - -0.2851674875666674
    - -0.9385475209128068
    Vapour pressure (Pa):
    - 7.1861
    - 0.5186
    - 0.1152
    cLogP:
    - 2.285000000003492
    - 1.206000000005588
    - 0.5590000000020154
    hCLAT CV75 (ug/mL):
    - .nan
    - 571.0951916
    - .nan
    hCLAT Call:
    - .nan
    - 0.0
    - .nan
    hCLAT EC150 (ug/mL):
    - .nan
    - .nan
    - .nan
    hCLAT EC200 (ug/mL):
    - .nan
    - .nan
    - .nan
    hCLAT MIT (ug/mL):
    - .nan
    - .nan
    - .nan
    kDPRA Call: []
    kDPRA Log rate (1/s/M):
    - .nan
    - .nan
    - .nan
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter   | Value   |
|------------------|---------|
| copy_X           | True    |
| fit_intercept    | True    |
| n_jobs           |         |
| positive         | False   |

</details>

### Model Plot

<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div>

## Evaluation Results

[More Information Needed]

# How to Get Started with the Model

[More Information Needed]

# Model Card Authors

This model card is written by following authors:

[More Information Needed]

# Model Card Contact

You can contact the model card authors through following channels:
[More Information Needed]

# Citation

Below you can find information related to citation.

**BibTeX:**
```
[More Information Needed]
```

# model_card_authors

Tomaz Mohoric

# limitations

This model is intended for educational purposes.

# model_description

This is a multiple linear regression model on a skin sensitisation dataset.