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Molecular feature generation for machine learning

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Molecular Descriptors

Molecular features for machine learning.

Table of Contents


This is a collection of methods to generate molecular features for machine learning, including common feautre representations like coulomb matrix etc. Also a graph generator for graph neural networks is found in molreps. This repo is currently under construction and can be easily expandend following this recommended style:

  • In molreps the main classes are listed
  • In methods individual functions are collected.
  • Uses a google-style doc string documentation for each function or class.


Clone repository and install with editable mode:

pip install -e ./molecular_features

or latest release via:

pip install molreps


Auto-documentation is generated at: .


Since there are many chemcial libraries in use, which can not be easily installed with pip (that include the following dependencies), they must be installed with e.g. conda:

  • rdkit, e.g. via conda install -c rdkit rdkit
  • openbabel, e.g. via conda install -c openbabel openbabel



Simple moleculear representations can be generated from molreps.descriptors.

from molreps.descriptors import coulomb_matrix
atoms = ['C','C']
coords = [[0,0,0],[1,0,0]]
cm = coulomb_matrix(atoms,coords)

However, also individual function can be used from molreps.methods. Like in this case the back-direction.

from molreps.methods.geo_npy import geometry_from_coulombmat
atom_cord = geometry_from_coulombmat(cm)


For many ML models a graph representation of the molecule is required. The module MolGraph from molreps.graph inherits from networkx's nx.Graph and can generate a molecular graph based on a mol-object provided by a cheminformatics package like rdkit, openbabel, ase etc. This is a flexible way to use functionalities from both networkx and packages like rdkit. First create a mol object.

import rdkit
m = rdkit.Chem.MolFromSmiles("CC1=CC=CC=C1")
m = rdkit.Chem.AddHs(m)

The mol object is passed to the MolGraph class constructor but can be further accessed.

import networkx as nx
import numpy as np
from molreps.graph import MolGraph
mgraph = MolGraph(m)
mgraph.mol  # Access the mol object.

The networkx graph is generated by make(), where the features and keys can be specified. There are pre-defined features that can be assigned by an identifier like 'key': 'identifier' or if further arguments are required by 'key' : {'class':'identifier', 'args':{'arg1': value1,'arg2': value2 }}. In the latter case also a custom function or class can be provided like 'key' : {'class': my_fun, 'args':{'arg1': value1,'arg2': value2 }}. A dictionary of predifined identifiers is listed in print(MolGraph._mols_implemented).

mgraph.make(nodes = {"AtomicNum" : 'AtomicNum'},
            edges = {"BondType" : 'BondType',
                     "Distance" : {'class':'Distance', 'args':{'bonds_only':True}}},
            state = {"ExactMolWt" : 'ExactMolWt'}

Note, a custom function must accept key and this instance as first argument, the molecule class is accessible via .mol. For example make a list of tuples such as [(i, {key: property})] for atoms and [((i,j, {key: property}))] for bonds and then add them to the graph by add_nodes_from() or add_edges_from(), respectively. Then the generated graph can be viewed and treated as a networkx graph, like plotting nx.draw(mgraph,with_labels=True). Finnaly, a closed form tensor is collected from selected features defined by the key-attribute. For each key an additional function to process the features and a default value can be optionally provided but defaults to np.array. A default value has to be added, if a single node or edge is missing a key, to generate a closed form tensor.

graph_tensors= mgraph.to_tensor(nodes = ["AtomicNum"],
                                edges = ["BondType"],
                                state = ["ExactMolWt"],
                                out_tensor = np.array)

The returned dictionary containing the feature tensors can be passed to graph models.


Examples scripts using this repo are collected in examples.


    author = {Patrick Reiser},
    title = {Python Package for Molecular Representations in Machine learning},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub Repository},
    howpublished = {\url{}},
    url = ""


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