Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design
Yuki Matsukiyo,
Atsushi Tengeiji,
Chen Li
et al.
Abstract: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 … Show more
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