Counting Tree-Like Multigraphs with a Given Number of Vertices and Multiple Edges

TL;DR

This paper presents a dynamic programming method to efficiently count tree-like multigraphs with specified vertices and multiple edges, aiding chemical graph enumeration for drug discovery.

Contribution

It introduces a novel recursive counting algorithm based on rooted multigraph representations, improving efficiency in enumerating complex chemical graphs.

Findings

Counts multigraphs with up to 170 vertices and 50 multiple edges in about 930 seconds

Provides an algorithm with quadratic time complexity in vertices and edges

Demonstrates effectiveness in chemical graph enumeration for drug discovery

Abstract

The enumeration of chemical graphs is an important topic in cheminformatics and bioinformatics, particularly in the discovery of novel drugs. These graphs are typically either tree-like multigraphs or composed of tree-like multigraphs connected to a core structure. In both cases, the tree-like components play a significant role in determining the properties and activities of chemical compounds. This paper introduces a method based on dynamic programming to efficiently count tree-like multigraphs with a given number $n$ of vertices and $\Delta$ multiple edges. The idea of our method is to consider multigraphs as rooted multigraphs by selecting their unicentroid or bicentroid as the root, and define their canonical representation based on maximal subgraphs rooted at the children of the root. This representation guarantees that our proposed method will not repeat a multigraph in the counting process. Finally, recursive relations are derived based on the number of vertices and multiple edges in the maximal subgraphs rooted at the children of roots. These relations lead to an algorithm with a time complexity of $\mathcal{O}(n^2(n + \Delta (n + \Delta^2 \cdot \min\{n, \Delta\})))$ and a space complexity of $\mathcal{O}(n^2(\Delta^3+1))$. Experimental results show that the proposed algorithm efficiently counts the desired multigraphs with up to 170 vertices and 50 multiple edges in approximately 930 seconds, confirming its effectiveness and potential as a valuable tool for exploring the chemical graph space in novel drug discovery.