Parallelization of enumerating tree-like chemical compounds by breadth-first search order

Area of Science:

• Computational chemistry
• Drug discovery
• Cheminformatics

Background:

• Accurate enumeration of chemical compounds is crucial for drug design and structural determination via mass spectrometry.
• Existing methods for chemical structure enumeration face challenges in efficiency and scope.
• Previous work introduced BfsSimEnum and BfsMulEnum algorithms for enumerating tree-like chemical compounds.

Purpose of the Study:

• To present and evaluate the efficiency of BfsSimEnum and BfsMulEnum algorithms for chemical compound enumeration.
• To demonstrate the advantages of these algorithms over existing methods in terms of speed and applicability.
• To highlight the utility of these algorithms in accelerating drug discovery and chemical structure elucidation.

Main Methods:

• Development of Breadth-First Search (BFS)-based algorithms: BfsSimEnum for compounds without multiple bonds and BfsMulEnum for compounds with multiple bonds.
• Implementation of efficient graph traversal and enumeration techniques.
• Comparative analysis against established chemical enumeration algorithms.

Main Results:

• BfsSimEnum and BfsMulEnum demonstrated superior performance in enumerating tree-like chemical structures.
• The algorithms achieved faster enumeration speeds compared to several existing methods across various datasets.
• The methods proved effective for both simple and complex chemical structures, including those with multiple bonds.

Conclusions:

• The developed BfsSimEnum and BfsMulEnum algorithms offer significant speed improvements for enumerating tree-like chemical compounds.
• These algorithms represent a valuable advancement for computational chemistry, aiding in drug design and mass spectrometry-based structure determination.
• The enhanced efficiency facilitates faster exploration of chemical space, accelerating the discovery of novel molecules.