Yu-Bin Hong  Kyungjun Lee  DongNyenog Heo  Heeyoul Choi

10.5626/JOK.2023.50.11.976

Drug Discovery molecule generation Deep Learning Transformer Language Model

Among various generative models, recurrent neural networks (RNNs) based models have achieved state-of-the-art performance in the drug generation task. To overcome the long-term dependency problem that RNNs suffer from, Transformer-based models were proposed for the task. However, the Transformer models showed worse performances than the RNNs models in the drug generation task, and we believe it was because the Transformer models were over-parameterized with the over-fitting problem. To avoid the problem, in this paper, we propose a new Transformer model by replacing the large decoder with simple feed-forward layers. Experiments confirmed that our proposed model outperformed the previous state-of-the-art baseline in major evaluation metrics while preserving other minor metrics with a similar level of performance. Furthermore, when we applied our model to generate candidate molecules against SARs-CoV-2 (COVID-19) virus, the generated molecules were more effective than drugs in commercial market such as Paxlovid, Molnupiravir, and Remdesivir.