Abstract
AbstractThe goal of drug design and discovery is to find new molecules with desirable properties. To this end, molecule generation is usually employed to generate novel molecules to build a virtual molecule library for further screening. With the rapid development of deep generative modeling techniques, researchers are now applying deep generative models, particularly Generative Adversarial Networks (GANs), for molecule generation. In this chapter, we try to survey the applications of GANs for molecule generation in drug design and discovery, to give the readers a relatively comprehensive picture of this area. Firstly, we introduce the preliminary concepts of molecule generation, including molecule representation and evaluation metrics. Then, we review major molecule generation models based on GANs developed in recent years. In addition, comparison between GAN-based models and those based on other deep generative models is conducted, and the limitations of GAN-based models are discussed. Finally, challenges and future directions in this area are highlighted.
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