QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease

Sebastian-Perez, Victor; Jimena Martinez, Maria; Gil, Carmen; Eugenia Campillo, Nuria; Martinez, Ana; Ponzoni, Ignacio

Abstract

Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucinerich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.

Más información

Título según WOS: ID WOS:000459384300003 Not found in local WOS DB
Título de la Revista: JOURNAL OF INTEGRATIVE BIOINFORMATICS
Volumen: 16
Número: 1
Editorial: WALTER DE GRUYTER GMBH
Fecha de publicación: 2019
DOI:

10.1515/jib-2018-0063

Notas: ISI