Comparison of State-of-the-art Algorithms for de novo Drug Design
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2020
Författare
Nilsson, Sebastian
Sundkvist, Jonathan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
During the last decade, the application of deep learning in de novo drug design
has increased. By employing generative models, in combination with suitable optimization
algorithms, the chemical space can be explored to generate new and useful
molecular compounds. Multiple models have been published for this purpose. While
they all report promising results on various optimization tasks, there is a lack of continuity
regarding what tasks on which the models are demonstrated. This makes
a comparison of the models unfeasible. In this thesis, we provide a comprehensive
evaluation and comparison of five state-of-the-art algorithms for de novo drug design.
To this end, the selected models have been submitted to two experiments that
evaluate their ability to generate and optimize molecules on a variety of different
optimization tasks. The reported results show that a generative language model displays
the best performance, both in terms of exploring and exploiting the chemical
space. An application of particle swarm optimization on a continuous representation
of the chemical space also shows promise, and we suggest further research on more
elaborate usages of this method.
Beskrivning
Ämne/nyckelord
Drug design , deep learning , generative model , optimization , variational autoencoder , language modelling , comparison