Introduction
In MCQDML algorithm, we have extended the
MCQD algorithm with an initial
phase of machine learningbased prediction of T limit parameter that is best suited for each input
graph. We show empirically that the resulting new algorithm, MCQDML improves search speeds
on certain types of graphs, most notably for molecular docking graphs that are used in drug
design where they determine energetically favorable conformations of small molecules in a
protein binding site. In such cases, the speedup is 2fold.
Download
The MCQDML algorithm source code is freely available at
GitHub.
Graphs
Small protein product graphs, full protein product graphs and docking graphs that were used in the paper are available:
Please Cite

Kristjan Reba, Matej Guid, Kati Rozman, Dušanka Janežič, Janez Konc. Exact Maximum Clique Algorithm for Different Graph Types Using Machine Learning. Mathematics, 2022, 10, 97. Open Access