Self-attention Mechanism in GANs for Molecule Generation

In this study, we address the challenge of generating longer molecules using discrete sequence-based Generative Adversarial Networks (GANs), where traditional approaches often falter. We introduce an innovative application of the Self-Attention mechanism within GANs, facilitating the model’s ability to account for long-range dependencies effectively. This method not only enhances the generation of novel molecular structures but also ensures these structures possess desirable properties, marking a significant step forward in the fields of computational chemistry and drug design.