Molecular De Novo Design through Transformer-based Reinforcement Learning

arXiv:2310.05365v5 Announce Type: replace
Abstract: 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 …

abstract arxiv capacity contrast cs.ai cs.lg design generate generative networks neural networks recurrent neural networks reinforcement reinforcement learning rnn through transformer transformers type work