Molecular De Novo Design through Transformer-based Reinforcement Learning
CoRR(2023)
摘要
In this work, we introduce a method to fine-tune a Transformer-based
generative model for molecular de novo design. Leveraging the superior sequence
learning capacity of Transformers over Recurrent Neural Networks (RNNs), our
model can generate molecular structures with desired properties effectively. In
contrast to the traditional RNN-based models, our proposed method exhibits
superior performance in generating compounds predicted to be active against
various biological targets, capturing long-term dependencies in the molecular
structure sequence. The model's efficacy is demonstrated across numerous tasks,
including generating analogues to a query structure and producing compounds
with particular attributes, outperforming the baseline RNN-based methods. Our
approach can be used for scaffold hopping, library expansion starting from a
single molecule, and generating compounds with high predicted activity against
biological targets.
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