In silico modeling of protein-ligand binding

Varela Rial, Alejandro

In silico modeling of protein-ligand binding

Document Type

Doctoral thesis
Published Version

Author

Varela Rial, Alejandro

Director

De Fabritiis, Gianni

Publication Date

2022-02-21

Pages

120 p.

Citation

Varela Rial, A. In silico modeling of protein-ligand binding. 2022. handle: http://hdl.handle.net/10803/673579

This citation was generated automatically.

Doctoral program

Programa de doctorat en Biomedicina

Abstract

The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with software tools would be very beneficial to drug discovery. In this thesis, several applications have been developed to model and predict the binding mode of a ligand to a protein, to evaluate the feasibility of that prediction and to perform model interpretability in deep neural networks trained on protein-ligand complexes.
La afinidad de un fármaco a su proteína diana es una de las propiedades clave de un fármaco. Actualmente, existen métodos experimentales para medir la afinidad, pero son muy costosos y relativamente lentos. Así, predecir esta propiedad con precisión empleando herramientas de software sería muy beneficioso para el descubrimiento de fármacos. En esta tesis se han desarrollado aplicaciones de software para modelar y predecir el modo de unión de ligando a proteína, para evaluar cómo de factible es tal predicción y para interpretar redes neuronales profundas entrenadas en complejos proteína-ligando.

Keywords

Docking, Neural networks, Explainable artificial intelligence, Pharmacophore, Modeling, Acoplamiento molecular, Redes neuronales, Inteligencia artificial explicable, Farmacóforo, Modelización