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# Table of contents:

- [Using DeepMol models](#using-deepmol-models)
- [How to use](#how-to-use)
- [Visualization](#visualization)
- [Case studies for the DeepMol publication](#case-studies-for-the-deepmol-publication)
- [AutoML experiments - TDC Commons](#automl-experiments---tdc-commons)
- [Comparison of DeepMol and QSARTuna](#comparison-of-deepmol-and-qsartuna)
- [Benchmark computational resources and runtimes of DeepMol](#benchmark-computational-resources-and-runtimes-of-deepmol)
- [Evaluate TDC commons benchmark datasets](#evaluate-tdc-commons-benchmark-datasets)

# 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:

```python
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 | CC[N+](C)(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O | 1.0 |
| 3 | CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[...| 0.0 |
| 4 | CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C(=O)O)c3ccsc3...| 0.0 |


Or pass SMILES strings and get the results in one dataframe:

```python
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 | CC[N+](C)(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O | 1.0 |

Complementarily, you can run several models:

```python
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 | CC[N+](C)(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]1O[C@H](CO)[C@@H](O)[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](NC(=O)CC#N)[...] | 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:

```python
from deepmol_models import bokeh_plot
bokeh_plot(results, "Solubility", additional_labels=["Pathways", "Superclass", "Class"])
```

![bokeh](https://github.com/BioSystemsUM/deepmol_case_studies/blob/main/example_bokeh.gif)

# Case studies for the DeepMol publication

## Installation
Expand Down Expand Up @@ -120,96 +224,3 @@ from dcs.evaluation import get_results

results = get_results(tdc_dataset_name="Bioavailability_Ma")
```

# 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:

```python
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 | CC[N+](C)(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O | 1.0 |
| 3 | CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[...| 0.0 |
| 4 | CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C(=O)O)c3ccsc3...| 0.0 |


Or pass SMILES strings and get the results in one dataframe:

```python
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 | CC[N+](C)(C)c1cccc(O)c1 | 0.0 |
| 2 | Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O | 1.0 |

Complementarily, you can run several models:

```python
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 | CC[N+](C)(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]1O[C@H](CO)[C@@H](O)[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](NC(=O)CC#N)[...] | 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:

```python
from deepmol_models import bokeh_plot
bokeh_plot(results, "Solubility", additional_labels=["Pathways", "Superclass", "Class"])
```

![bokeh](example_bokeh.gif)

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