An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening
| Autor: | Giovanni Xavier Perazzo, Ana T. Winck, Adriano Velasque Werhli, Karina S. Machado, Vinicius Seus |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Models
Molecular Article Subject Computer science Decision tree Drug Evaluation Preclinical lcsh:Medicine Drug design Bioinformatics computer.software_genre Ligands General Biochemistry Genetics and Molecular Biology Set (abstract data type) User-Computer Interface C4.5 algorithm HIV Protease Molecular descriptor Data Mining Selection (genetic algorithm) Virtual screening General Immunology and Microbiology lcsh:R Decision Trees Reproducibility of Results General Medicine Data warehouse Data mining computer Research Article |
| Zdroj: | BioMed Research International Repositório Institucional da FURG (RI FURG) Universidade Federal do Rio Grande (FURG) instacron:FURG BioMed Research International, Vol 2014 (2014) |
| ISSN: | 2314-6141 2314-6133 |
| Popis: | The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands. |
| Databáze: | OpenAIRE |
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