molecules.js
A chemical graph theory library for JavaScript. Latest demo of molecules.js + d3.js here.
Features
- Import molecules encoded with SMILES chemical line notation.
- Compute various graph matrices of a molecule (e.g. adjacency, degree, distance, Laplacian, Randic, reciprocal).
- Compute several topological indices of a molecule (e.g. Balaban, Harary, Hyper-Wiener, Randic, Wiener).
- Visualize molecules with
d3.jsforce directed graphs.
Getting Started
The Molecules module contains the primary functions for loading, saving, computing graph matrices and computing topological indices.
var Molecules = require('molecules.js');Introduction
A molecule is a graph comprised of nodes (atoms) and edges (bonds). In molecules.js a molecule is an object with the following schema:
molecule = {
id : Number,
name : String,
atoms : Object,
bonds : Object,
properties : {
mass : Number,
formula : Object
}
}Loading Molecules
// Create a molecule by parsing a SMILES string
var molecule = Molecules.load.smiles('NCC(O)O');// Import a molecule from a JSON file
var molecule = Molecules.load.json(url);Saving Molecules
// Convert a molecule to JSON format
var data = Molecules.save.json(molecule);// Convert a molecule to a d3 graph object
var graph = Molecules.save.d3(molecule);Graph Matrices
// Load a molecule of ethanol ('CCO')
var ethanol = Molecules.load.smiles('CCO');Adjacency Matrix
// Compute the adjacency matrix of ethanol
var adjacencyMatrix = Molecules.topology.matrix.adjacency(ethanol);
// C C O
// C [ 0, 1, 0 ]
// C [ 1, 0, 1 ]
// O [ 0, 1, 0 ]Distance Matrix
// Use the adjacency matrix to compute the distance matrix
var distanceMatrix = Molecules.topology.matrix.distance(adjacencyMatrix);
// C C O
// C [ 0, 1, 2 ]
// C [ 1, 0, 1 ]
// O [ 2, 1, 0 ]Degree Matrix
// Use the adjacency matrix to compute the degree matrix
var degreeMatrix = Molecules.topology.matrix.degree(adjacencyMatrix);
// C C O
// C [ 1, 0, 0 ]
// C [ 0, 2, 0 ]
// O [ 0, 0, 1 ]Reciprocal Matrix
// Use the distance matrix to compute the reciprocal matrix
var reciprocalMatrix = Molecules.topology.matrix.reciprocal(distanceMatrix);
// C C O
// C [ 0.0, 1.0, 0.5 ]
// C [ 1.0, 0.0, 1.0 ]
// C [ 0.5, 1.0, 0.0 ]Laplacian Matrix
// Use the adjacency and degree matrix to compute the Laplacian matrix
var laplacianMatrix = Molecules.topology.matrix.laplacian(adjacencyMatrix, degreeMatrix);
// C C O
// C [ 1, -1, 0 ]
// C [ -1, 2, -1 ]
// C [ 0, -1, 1 ]Randic Matrix
// Use the adjacency and degree matrix to compute the Randic matrix
var randicMatrix = Molecules.topology.matrix.randic(adjacencyMatrix, degreeMatrix);
// C C O
// C [ 0.000, 0.707, 0.000 ]
// C [ 0.707, 0.000, 0.707 ]
// O [ 0.000, 0.707, 0.000 ]Topological Indices
// Load a molecule of ethanol ('CCO')
var ethanol = Molecules.load.smiles('CCO');
// Compute the following graph matrices
var adjacencyMatrix = Molecules.topology.matrix.adjacency(ethanol);
var distanceMatrix = Molecules.topology.matrix.distance(adjacencyMatrix);
var degreeMatrix = Molecules.topology.matrix.degree(adjacencyMatrix);
var reciprocalMatrix = Molecules.topology.matrix.reciprocal(distanceMatrix);
var randicMatrix = Molecules.topology.matrix.randic(adjacencyMatrix, degreeMatrix);Wiener Index
// Use the distance matrix to compute the Wiener index
var wienerIndex = Molecules.topology.index.wiener(distanceMatrix);
// 4.0Hyper-Wiener Index
// Use the distance matrix to compute the Hyper-Wiener index
var hyperwienerIndex = Molecules.topology.index.hyperwiener(distanceMatrix);
// 5.0Harary Index
// Use the reciprocal matrix to compute the Harary index
var hararyIndex = Molecules.topology.index.harary(reciprocalMatrix);
// 2.5Balaban Index
// Use the distance matrix to compute the Balaban index
var balabanIndex = Molecules.topology.index.balaban(distanceMatrix);
// 1.632993Randic Index
// Use the Randic matrix to compute the Randic index
var randicIndex = Molecules.topology.index.randic(randicMatrix);
// 1.414213