Molecules, Vol. 29, Pages 1499: Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery

Molecules, Vol. 29, Pages 1499: Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery Molecules doi: 10.3390/molecules29071499 Authors: Chun Zhang Liangxu Xie Xiaohua Lu Rongzhi Mao Lei Xu Xiaojun Xu 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 by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The propose...
Source: Molecules - Category: Chemistry Authors: Tags: Article Source Type: research