Using DeepMol models
Models available so far:
| Model Name | How to Call | Prediction Type |
|---|---|---|
| BBB (Blood-Brain Barrier) | BBB |
Penetrates BBB (1) or does not penetrate BBB (0) |
| AMES Mutagenicity | AMES |
Mutagenic (1) or not mutagenic (0) |
| Human plasma protein binding rate (PPBR) | PPBR |
Rate of PPBR expressed in percentage |
| Volume of Distribution (VD) at steady state | VDss |
Volume of Distribution expressed in liters per kilogram (L/kg) |
| Caco-2 (Cell Effective Permeability) | Caco2 |
Cell Effective Permeability (cm/s) |
| HIA (Human Intestinal Absorption) | HIA |
Absorbed (1) or not absorbed (0) |
| Bioavailability | Bioavailability |
Bioavailable (1) or not bioavailable (0) |
| Lipophilicity | Lipophilicity |
Lipophilicity log-ratio |
| Solubility | Solubility |
Solubility (log mol/L) |
| CYP P450 2C9 Inhibition | CYP2C9Inhibition |
Inhibit (1) or does not inhibit (0) |
| CYP P450 3A4 Inhibition | CYP3A4Inhibition |
Inhibit (1) or does not inhibit (0) |
| CYP2C9 Substrate | CYP2C9Substrate |
Metabolized (1) or does not metabolize (0) |
| CYP2D6 Substrate | CYP2D6Substrate |
Metabolized (1) or does not metabolize (0) |
| CYP3A4 Substrate | CYP3A4Substrate |
Metabolized (1) or does not metabolize (0) |
| Hepatocyte Clearance | HepatocyteClearance |
Drug hepatocyte clearance (uL.min-1.(10^6 cells)-1) |
| NPClassifier | NPClassifier |
Pathway, Superclass, Class |
| Plants secondary metabolite precursors predictor | PlantsSMPrecursorPredictor |
Precursor 1; Precursor 2 |
| Microsome Clearance | MicrosomeClearance |
Drug microsome clearance (mL.min-1.g-1) |
| LD50 | LD50 |
LD50 (log(1/(mol/kg))) |
| hERG Blockers | hERGBlockers |
hERG blocker (1) or not blocker (0) |
How to use:
You can use them either individually or mixed together.
You can call one model individually, pass a CSV file and get the results in one dataframe:
from deepmol_models import BBB
results = BBB().predict_from_csv("dataset.csv", smiles_field="Drug", id_field="Drug_ID", output_file="predictions.csv")
results
| ID | SMILES | BBB Penetration |
|---|---|---|
| 0 | OCC(S)CS | 1.0 |
| 1 | CCN+(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1OC@HC@@H[C@@H]1O | 1.0 |
| 3 | CC(=O)OCC1=C(C(=O)O)N2C(=O)C@@H[... | 0.0 |
| 4 | CC1(C)S[C@@H]2[C@H](NC(=O)C@Hc3ccsc3... | 0.0 |
Or pass SMILES strings and get the results in one dataframe:
from deepmol_models import BBB
results = BBB().predict_from_csv("dataset.csv", smiles_field="Drug", id_field="Drug_ID", output_file="predictions.csv")
results
| ID | SMILES | BBB Penetration |
|---|---|---|
| 1 | CCN+(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1OC@HC@@H[C@@H]1O | 1.0 |
Complementarily, you can run several models:
from deepmol_models import BBB, PPBR, VDss, Caco2, HIA, Bioavailability, \
Lipophilicity, Solubility, PlantsSMPrecursorPredictor, NPClassifier, MixedPredictor
# results = MixedPredictor([BBB(), Caco2(), CYP2D6Inhibition(), NPClassifier()]).predict_from_csv("test_molecules.csv", "Drug", "Drug_ID", output_file="predictions.csv")
results = MixedPredictor([BBB(), PPBR(), VDss(), Caco2(),
HIA(), Bioavailability(), Lipophilicity(),
Solubility(), PlantsSMPrecursorPredictor(), NPClassifier()]).predict_from_csv("test_molecules.csv", smiles_field="Drug", id_field="Drug_ID", output_file="predictions.csv")
results
| ID | SMILES | BBB Penetration | Human PPBR | VDss | Cell Effective Permeability | Human Intestinal Absorption | Bioavailability | Lipophilicity | Solubility | Precursors | Pathways | Superclass | Class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | OCC(S)CS | 1.0 | 64.832665 | 5.803529 | -4.725497 | 0.0 | 0.0 | 0.290602 | 0.117866 | Fatty acyls | Fatty alcohols | Fatty alcohols | |
| 1 | CCN+(C)c1cccc(O)c1 | 0.0 | 32.882912 | 2.891243 | -4.989814 | 0.0 | 0.0 | 0.271549 | -0.606772 | L-Lysine | Alkaloids | Tyrosine alkaloids | Phenylethylamines |
| 2 | Nc1ncnc2c1ncn2[C@@H]1OC@HC@@H[C@@H]1O | 1.0 | 41.540812 | 2.722000 | -6.059630 | 1.0 | 0.0 | 0.250526 | -1.701435 | Dimethylallyl diphosphate | Carbohydrates | Nucleosides | Purine nucleosides |
| 3 | CC(=O)OCC1=C(C(=O)O)N2C(=O)C@@H[...] | 0.0 | 52.921825 | 0.329071 | -5.473660 | 0.0 | 0.0 | 0.267842 | -2.512460 | Geranylgeranyl diphosphate; L-Alanine | Amino acids and Peptides | β-lactams | Cephalosporins |
Visualization
You can use our API to access the bokeh representation of the chemical space and check some features of the molecules:
from deepmol_models import bokeh_plot
bokeh_plot(results, "Solubility", additional_labels=["Pathways", "Superclass", "Class"])
1 maintainer
Metadata
1 maintainer
Release files for deepmol-models 0.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| deepmol_models-0.0.5.tar.gz | 30.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deepmol_models-0.0.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 61.8 kB
Release files / deepmol_models-0.0.5.tar.gz
| Download URL | deepmol_models-0.0.5.tar.gz |
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| Size | 30.4 kB |
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