Molecular De Novo Design through Transformer-based Reinforcement Learning
This work addresses the problem of efficient drug discovery for pharmaceutical researchers, representing an incremental improvement over existing methods.
The researchers tackled molecular de novo design by fine-tuning a Transformer-based generative model, which outperformed traditional RNN-based methods in generating compounds with desired properties, such as high predicted activity against biological targets.
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.