| Autor: |
Antonio Bolufé-Röhler, Alex Coto-Santiesteban, Marta Rosa Soto, Stephen Chen |
| Jazyk: |
English<br />Spanish; Castilian |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología, Vol 2, Iss 3 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2255-5684 |
| DOI: |
10.5281/zenodo.7080714 |
| Popis: |
Computer modeling of protein ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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