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2024 | OriginalPaper | Buchkapitel

AI Deep Learning Generative Models for Drug Discovery

verfasst von : Qifeng Bai, Jian Ma, Tingyang Xu

Erschienen in: Applications of Generative AI

Verlag: Springer International Publishing

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Abstract

Artificial intelligence (AI) deep learning generative models play an increasingly important role in drug design. Developments of different drug generative models can save capital and time to promote new drug discovery. AI deep learning generative models can be divided into different generative models based on the different levels of dimensional features of receptors and ligands such as SMILES generative models, molecular graph generative models, and 3D molecule generative models. Besides, based on the different algorithms, AI deep learning generative models for drug discovery can be roughly classified as variational autoencoder generative model, generative adversarial network generative model, and flow based generative model, and diffusion generative model. In this chapter, the classification, general mathematical methods, and research reports of AI deep learning generative models are summarized based on the different levels of dimensional features and algorithms. This chapter proposes an interesting topic and a deep understanding of AI deep learning generative models for the scientific community.

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Titel: AI Deep Learning Generative Models for Drug Discovery
verfasst von: Qifeng Bai
Jian Ma
Tingyang Xu
Verlag: Springer International Publishing
Buch: Applications of Generative AI
Print ISBN: 978-3-031-46237-5

Electronic ISBN: 978-3-031-46238-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-46238-2_23

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