Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors.

ABSTRACT: The de novo drug design based on SMILES format is a typical sequence-processing problem. Previous methods based on recurrent neural network (RNN) exhibit limitation in capturing long-range dependency, resulting in a high invalid percentage in generated molecules. Recent studies have shown the potential of Transformer architecture to increase the capacity of handling sequence data. In this work, the encoder module in the Transformer is used to build a generative model. First, we train a Transformer-encoder-based generative model to learn the grammatical rules of known drug molecules and a predictive model to predict the activity of the molecules. Subsequently, transfer learning and reinforcement learning were used to fine-tune and optimize the generative model, respectively, to design new m

SUBMITTER: Yang L 

PROVIDER: S-EPMC8674994 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature