20.500.12556/RUL-136784 Exact maximum clique algorithm for different graph types using machine learning Finding a maximum clique is important in research areas such as computational chemistry, social network analysis, and bioinformatics. It is possible to compare the maximum clique size between protein graphs to determine their similarity and function. In this paper, improvements based on machine learning (ML) are added to a dynamic algorithm for finding the maximum clique in a protein graph, Maximum Clique Dynamic (MaxCliqueDyn; short: MCQD). This algorithm was published in 2007 and has been widely used in bioinformatics since then. It uses an empirically determined parameter, Tlimit, that determines the algorithm’s flow. We have extended the MCQD algorithm with an initial phase of a machine learning-based prediction of the Tlimit parameter that is best suited for each input graph. Such adaptability to graph types based on state-of-the-art machine learning is a novel approach that has not been used in most graph-theoretic algorithms. We show empirically that the resulting new algorithm MCQD-ML improves search speed on certain types of graphs, in particular molecular docking graphs used in drug design where they determine energetically favorable conformations of small molecules in a protein binding site. In such cases, the speed-up is twofold. maximum clique protein graphs machine learning ProBiS kemija algoritmi strojno učenje matematika grafi MCQD true false true Angleški jezik Slovenski jezik Članek v reviji 2022-05-20 12:47:28 2022-05-20 12:47:29 2022-09-15 04:14:19 0000-00-00 00:00:00 2022 0 0 14 str. iss. 1, art. 97 Vol. 10 Jan. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 54 2227-7390 10.3390/math10010097 94170115 mathematics-10-00097-v2.pdf mathematics-10-00097-v2.pdf 1 B7E9FB055C0F56648F1481336AB9CA75 294b8f19289549cacbaa413277099774522b39f25fa90dfd3803a5fbd1471fa0 53671d20-d82a-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156571 https://www.mdpi.com/2227-7390/10/1/97 1 2049beb8-d82a-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156570 Fakulteta za računalništvo in informatiko 0 0 0