An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design. (arXiv:2110.05478v1 [q-bio.QM])

As a promising tool to navigate in the vast chemical space, artificial
intelligence (AI) is leveraged for drug design. From the year 2017 to 2021, the
number of applications of several recent AI models (i.e. graph neural network
(GNN), recurrent neural network (RNN), variation autoencoder (VAE), generative
adversarial network (GAN), flow and reinforcement learning (RL)) in drug design
increases significantly. Many relevant literature reviews exist. However, none
of them provides an in-depth summary of many applications of the recent AI
models in drug design. To complement the existing literature, this survey
includes the theoretical development of the previously mentioned AI models and
detailed summaries of 42 recent applications of AI in drug design. Concretely,
13 of them leverage GNN for molecular property prediction and 29 of them use RL
and/or deep generative models for molecule generation and optimization. In most
cases, the focus of the summary is the models, their variants, and
modifications for specific tasks in drug design. Moreover, 60 additional
applications of AI in molecule generation and optimization are briefly
summarized in a table. Finally, this survey provides a holistic discussion of
the abundant applications so that the tasks, potential solutions, and
challenges in AI-based drug design become evident.



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