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Table of contents:

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 get information about each model just by instatiating the class.

from deepmol_models import NPClassifier

NPClassifier()

NPClassifier Overview

This model is a reimplementation of NPClassifier as described in the ACS journal publication.

All credits should be given to the authors. NPClassifier performs automated structural classification of natural products.

NPClassifier Details

Attribute Value
Model Name NPClassifier
Prediction Type Pathway, Superclass, Class

Key Features

  • Prediction of natural product Pathway, Superclass, and Class.
  • Efficient performance on large datasets.

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"])

bokeh

Example

You can find an example in example.

Case studies for the DeepMol publication

Installation

  1. Clone the repository and move into the directory:
git clone https://github.com/BioSystemsUM/deepmol_case_studies.git
cd deepmol_case_studies
  1. Create a conda environment and activate it:
conda create -n deepmol_case_studies python=3.10
conda activate deepmol_case_studies
  1. Install the dependencies:
pip install -r requirements.txt
pip install --no-deps deepmol[all]==1.1.7
  1. Install the package:
pip install .

AutoML experiments - TDC Commons

AutoML experiments can be found in here.

We used podman/docker for the experiments, the Dockerfile can be found in this repository.

The "run" file can be found in here.

Comparison of DeepMol and QSARTuna

AutoML experiments for comparison between DeepMol and QSARTuna can be found in here.

We used podman/docker for the experiments, the Dockerfile can be found in this repository.

The "run_automl_benchmark" file can be found in here.

Benchmark computational resources and runtimes of DeepMol

The scripts to evaluate computational resources and runtimes of each method in DeepMol are here.

All the dataframes with this information are available here.

Evaluate TDC commons benchmark datasets

To train and evaluate DeepMol models:

from dcs.evaluation import get_results

results = get_results(tdc_dataset_name="Bioavailability_Ma", pipeline="bioavailability_optimal")

The tdc_dataset_name parameter is used to download the TDC commons benchmark datasets. Available datasets:

  • "AMES"
  • "BBB_Martins"
  • "Bioavailability_Ma"
  • "Caco2_Wang"
  • "Clearance_Hepatocyte_AZ"
  • "Clearance_Microsome_AZ"
  • "HIA_Hou"
  • "Pgp_Broccatelli"
  • "Solubility_AqSolDB"
  • "Lipophilicity_AstraZeneca"
  • "VDss_Lombardo"
  • "CYP2C9_Veith"
  • "CYP2D6_Veith"
  • "CYP3A4_Veith"
  • "CYP2C9_Substrate_CarbonMangels"
  • "CYP2D6_Substrate_CarbonMangels"
  • "CYP3A4_Substrate_CarbonMangels"
  • "DILI"
  • "Half_Life_Obach"
  • "hERG"
  • "LD50_Zhu"
  • "PPBR_AZ"

While the pipeline parameter is for internal pipeline loading. The pipelines are listed according to the paper, where there are the pipelines created based on the first AutoML experiment and the ones that were further optimized (optimal). Available pipelines:

  • "ames"
  • "bbb"
  • "bioavailability"
  • "bioavailability_optimal"
  • "caco"
  • "clearance_hepatocyte"
  • "clearance_microsome"
  • "hia"
  • "pgp"
  • "solubility"
  • "lipophilicity"
  • "lipophilicity_optimal"
  • "vdss"
  • "cyp2c9"
  • "cyp2d6"
  • "cyp3a4"
  • "cyp2c9_substrate"
  • "cyp2d6_substrate"
  • "cyp3a4_substrate"
  • "dili"
  • "half_life"
  • "herg"
  • "hia"
  • "ld50"
  • "ppbr"

If intended, the default pipelines (all but the optimal) for each dataset can be called as follows:

from dcs.evaluation import get_results

results = get_results(tdc_dataset_name="Bioavailability_Ma")