Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

ABSTRACT: Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance b

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11013495 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature