Algoritmo K-NN para la identificación de posibles fármacos contra la COVID-19
Palabras clave:
ChEMBL, clústers, K-NN, quimioinformática, SARS-CoV-2Resumen
El objetivo de la investigación explorar y validar la aplicación del algoritmo K-NN para la identificación de grupos de compuestos que pueden ser empleadas contra la COVID-19 mediante métodos de quimioinformática. Para lograrlo, se analizaron los componentes de la base de datos ChEMBL empleados en estudios experimentales sobre el SARS-CoV-2. Esta información fue analizada de forma manual y, finalmente, se obtuvieron 1904 biomoléculas categorizadas como “Activas” o “Inactivas” en función de su actividad inhibitoria frente a dicho virus. Después, se empleó un algoritmo de K-vecinos más cercano (K-NN) para agrupar las biomoléculas en función de su similitud fisicoquímica. Finalmente, el estudio evidenció que este tipo de algoritmos es una herramienta valiosa para identificar posibles compuestos iniciales para posteriores investigaciones que ayuden a combatir la COVID-19, estableciendo de esta manera una base metodológica para futuros trabajos en el presente tema.
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