De Novo Molecular Generation of Molecules with Consistent Synthetic Strategy
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Ekborg, Albin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The discovery of new pharmaceutical compounds is essential for developing novel medicines,
a process that is both costly and time-consuming. In this context, generative Artificial Intelli gence (AI) methods offer a valuable, cost-effective approach for generating large amounts of
de novo pharmaceutical candidates. However, these drug candidates must still be synthesized
for use in in vitro and in vivo studies, necessitating the creation of efficiently synthesizable
drug candidates. In this thesis, we explore two AI models for drug design developed by As traZeneca: REINVENT and AiZynthFinder. REINVENT is a generative AI model designed
for creating small molecules fine-tuned with Reinforcement Learning (RL), whereas AiZyn thFinder is a framework for retrosynthesis. We explore the combination of these models
through two novel reward functions and demonstrate that REINVENT, through querying
of AiZynthFinder, can be conditioned to generate molecules that share an identical synthetic
route. This is done in two ways, by provision of a template reaction, or by letting REINVENT
independently find identical routes, allowing the generated molecules to be synthesized in
parallel.
Beskrivning
Ämne/nyckelord
REINVENT, AIZYNTHFINDER, DEEP LEARNING, DRUG DESIGN.