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Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.

Gian Marco GhiandoniStuart R FlanaganMichael J BodkinMaria Giulia NiziAlbert Galera-PratAnnalaura BraiBeining ChenJames E A WallaceDimitar HristozovJames WebsterGiuseppe ManfroniLari LehtiöOriana TabarriniValerie J Gillet
Published in: Molecular informatics (2024)
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.
Keyphrases
  • deep learning
  • dna damage
  • dna repair
  • systematic review
  • machine learning
  • brain injury
  • convolutional neural network
  • functional connectivity
  • resting state
  • electron transfer