Sampling a Subset of Chemical Space with GNN-Based Generative Models
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Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
RASTEMO, TOBIAS
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In recent years deep neural network models have been used in the field of drug
discovery for de novo molecular design. One, somewhat novel, field of deep learning
that has seen some use in drug discovery is graph neural networks (GNN:s). This
thesis evaluates 6 GNN models for use in molecular graph generation. The evaluation
is based on a benchmark introduced by Arús-Pous et al. [1], which measures how
well models sample a subset of chemical space. The models are also compared to
existing recurrent neural network models (RNN:s), which use string representation of
molecules. The best performing GNN models achieve comparable scores to the RNN
models, all though the RNN models score higher. Even though the GNN models
score slightly lower on two of the training sets, they still show great potential for
future use and merit further research. In addition to this, a data loading scheme
for PyTorch is introduced, which increases training speed by loading training data
from disk efficiently.
Beskrivning
Ämne/nyckelord
machine learning , deep learning , graph neural networks , message passing neural network , de novo molecular design , graph generation