Abstract
Parkinson’s disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich 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.
Highlights
Background and MotivationAt present, the pursuit of effective treatments for neurodegenerative disorders is one of the imperative clinical and social needs
The database is a compilation of 67 molecules formerly synthesized in our research group and tested in leucine-rich repeat kinase 2 (LRRK2) enzyme [19]
In this assay LRRK2 kinase activity was measured and the results expressed as the percentage of enzyme inhibition for every compound, as it is further discussed in the previous reference
Summary
Background and MotivationAt present, the pursuit of effective treatments for neurodegenerative disorders is one of the imperative clinical and social needs. Parkinson’s Disease (PD) is the second most frequent human neurodegenerative disorder in persons over 60 years of age, affecting 1 in 100 people and growing to that affects 2–3% of the population ≥65 years of age. It is linked with Lewy bodies, abnormal aggregates of α-synuclein protein, and loss of dopaminergic neurons in the substantia nigra. LRRK2 is one of the most pursuing and auspicious targets for the future pharmacological treatment of PD In this regards, vast efforts are being done both from academia and pharmaceutical industry with the aim of designing selective and brain-permeable LRRK2 inhibitors as a strategy for PD [2], [3]. Several studies revealed that LRRK mutations raises aggregation of α-synuclein in dopaminergic neurons that are exposed to α-synuclein fibrils [6]
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