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Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design.

Yuki MatsukiyoAtsushi TengeijiChen LiYoshihiro Yamanishi
Published in: Journal of chemical information and modeling (2024)
Computational molecular generation methods that generate chemical structures from gene expression profiles have been actively developed for de novo drug design. However, most omics-based methods involve complex models consisting of multiple neural networks, which require pretraining. In this study, we propose a straightforward molecular generation method called GxRNN (gene expression profile-based recurrent neural network), employing a single recurrent neural network (RNN) that necessitates no pretraining for omics-based drug design. Specifically, our method utilizes the desired gene expression profile as input for the RNN, conditioning it to generate molecules likely to induce a similar profile. In a case study involving ten target proteins, GxRNN exhibited superior structural reproducibility of known ligands, surpassing several existing methods. This advancement positions our proposed method as a promising tool for facilitating de novo drug design.
Keyphrases
  • neural network
  • copy number
  • genome wide
  • adverse drug
  • genome wide identification
  • single cell
  • drug induced
  • dna methylation
  • gene expression
  • mass spectrometry
  • transcription factor