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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)
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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