chemfeat 2023.4

Calculate feature vectors for molecules using cheminformatics libraries.

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title: README author: Jan-Michael Rye

Synopsis

Generate molecular feature vectors for machine- and deep-learning models using cheminformatics software packages. A configuration file is used to select and configure different feature sets for inclusion in the full feature vector. The full list of available feature sets is available here. The results of feature set calculations are cached in an SQLite database to avoid the overhead of redundant calculations when feature sets are re-used.

The following packages are currently used to calculate chemical features:

• RDKit
• PaDEL-pywrapper

Links

• GitLab
• Documentation
• Python Package Index

Installation

The package can be installed from the Python Package Index with any compatible Python package manager, e.g.

pip install chemfeat

To install from source, clone the Git repository and install the package directly:

git clone https://gitlab.inria.fr/jrye/chemfeat.git
pip install ./chemfeat

Usage

Command-Line

The package provides the chemfeat command-line tool to generated CSV files of feature vectors from lists of InChi strings. It can also be used to generate a template feature-set configuration file and a markdown document describing all of the feature sets. The command's various help messages can be found here.

Usage Example

Given a feature set configuration file ("feature_sets.yaml") and a CSV file with a column of InChi strings ("inchis.csv"), a CSV file out features ("features.csv") can be generated with the following command:

chemfeat calc feature_sets.yaml inchis.csv features.csv

The following sections contain example contents for the input files and the output file that they produce.

feature_sets.yaml

Example feature set configuration file. Note that the feature sets are specified as a list, which allows the same feature set to be use multiple times with different parameters. For the full list of features, see the feature descriptions and the configuration file template.

# QED feature calculator.
- name: qed

# RDK descriptor feature calculator.
- name: rdkdesc

inchis.csv

Example CSV input file with a column containing InChi values. The name of the InChi column is configurable and defaults to "InChi".

InChi,name
"InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)","paracetamol"
"InChI=1S/C13H18O2/c1-9(2)8-11-4-6-12(7-5-11)10(3)13(14)15/h4-7,9-10H,8H2,1-3H3,(H,14,15)","ibuprofen"

featurs.csv

The CSV feature file that results from the example input files above.

InChi,qed__ALERTS,qed__ALOGP,qed__AROM,qed__HBA,qed__HBD,qed__MW,qed__PSA,qed__ROTB,rdkdesc__FpDensityMorgan1,rdkdesc__FpDensityMorgan2,rdkdesc__FpDensityMorgan3,rdkdesc__MaxAbsPartialCharge,rdkdesc__MaxPartialCharge,rdkdesc__MinAbsPartialCharge,rdkdesc__MinPartialCharge,rdkdesc__NumRadicalElectrons,rdkdesc__NumValenceElectrons
"InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)",2,2.0000999999999998,1,2,2,151.16500000000002,52.82000000000001,1,1.2727272727272727,1.8181818181818181,2.272727272727273,0.5079642937129114,0.18214293782620056,0.18214293782620056,-0.5079642937129114,0,58
"InChI=1S/C13H18O2/c1-9(2)8-11-4-6-12(7-5-11)10(3)13(14)15/h4-7,9-10H,8H2,1-3H3,(H,14,15)",0,3.073200000000001,1,2,1,206.28499999999997,37.3,4,1.2,1.7333333333333334,2.1333333333333333,0.4807885019257389,0.3101853515323108,0.3101853515323108,-0.4807885019257389,0,82

Python API

from chemfeat.database import FeatureDatabase
from chemfeat.features.manager import FeatureManager

# Here we assume that the following variables have already been defined:
# 
# feat_specs:
#   A list of feature specifications as returned by loading a YAML feature-set
#   configuration file.
#
# inchis:
#   An iterable of InChi strings representing the molecules for which the
#   features should be calculated.


# Create the database object.
feat_db = FeatureDatabase('features.sqlite')

# Create the feature manager object.
feat_man = FeatureManager(feat_db, feat_specs)

# Calculate the features and retrieve them as a Pandas dataframe.
feat_dataframe = feat_man.calculate_features(inchis, return_dataframe=True)