Tesis
Deep learning para seleção de conformações de proteínas considerando suas propriedades tridimensionais
Fecha
2018-08-27Autor
Silva, Raphael Giordano do Nascimento e
Institución
Resumen
A new drug discovery is financially costly and time-consuming. To deal this issue, the Rational
Drug Design process investigates the macromolecular interaction between receptor
molecules and drug-candidate ligands. Through molecular docking experiments it becomes
possible to evaluate the binding quality between these molecules. To simulate the flexibility
of the receptor, molecular dynamics simulations can be performed, where the protein
is represented by a distinct conformation at each instant of time. Thus, molecular docking
experiments can be performed on these different conformations. Several machine learning
techniques can be used to mine these data. This work presents an approach for artificial
neural networks deep learning, which uses as input several conformation about a given
protein, generated through molecular dynamic simulations. These conformation are described
in terms of its atoms tridimensional coordinates, labeled by a target attribute FEB,
which points out the quality about each conformation in molecular docking experiments. In
the deep learning approaches proposed in this work, we consider as input both these raw
data, as well as we consider an approach that makes use of clustering experiments that
generates a good parallelepiped surface for each atom, instead of the raw data. These
strategies are implemented in terms deep feedforward networks and deep convolutional
networks algorithms. For both architectures, the clustering-based strategy showed up promising
results. However, models generated through deep feedforward showed up the best
global results, being that those with the DBSCAN-Clustering-based approach highlight from
the other ones.