Self-attention Mechanism in GANs for Molecule Generation
Published in International Conference for Machine Learning and Applications (ICMLA), 2021
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.
Recommended citation: Chinnareddy, S., Grandhi, P., & Narayan, A. (2021, December). Self-attention mechanism in gans for molecule generation. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 57-60). IEEE. (https://ieeexplore.ieee.org/abstract/document/9680241)