Aplicaciones de las Matemáticas

Miércoles 23 de enero de 2019
16:00hrs

Aula 2


Imparte(n)

  • Dr. Fabien PLISSON
    (National Laboratory in Genomics for Biodiversity LANGEBIO-CINVESTAV IPN)

Responsable(s):

  • Jesús Igor Heberto Barahona Torres
  • Gilberto Calvillo Vives

Resumen:

The recent success of small-molecule kinase inhibitors as anticancer drugs has generated significant interest in their application to other clinical areas, such as disorders of the central nervous system (CNS). However, most kinase inhibitor drug candidates investigated to date have been ineffective at treating CNS disorders, mainly due to poor blood-brain barrier (BBB) permeability. It is therefore imperative to evaluate new lead compounds for both kinase inhibition and BBB permeability. Over the last 35 years, marine biodiscovery has yielded 471 natural products reported as kinase inhibitors, yet very few have been evaluated for BBB permeability. 

In this study, we revisited these marine natural products and predicted their ability to cross the BBB by applying freely available open-source chemoinformatics and machine learning algorithms to a training set of 332 previously reported CNS-penetrant small molecules. We evaluated several regression and classification models and found that our optimised classifiers (random forest, gradient boosting and logistic regression) outperformed other models, with overall cross-validated model accuracies of 80-82% and 78-80% on external testing. All 3 binary classifiers predicted 13 marine-derived kinase inhibitors with appropriate physicochemical characteristics for BBB permeability.

This talk will briefly introduce the audience to the field of chemoinformatics applied to drug discovery prior to present in details the application of predictive models for BBB permeability to natural products.

Keywords: marine natural products, kinase inhibitors, blood-brain barrier permeability, neurological disorders, machine learning, QSPR, RDKit


Compartir este seminario