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TitleGenerative deep learning for targeted compound design
Author(s)Sousa, Tiago
Correia, João
Pereira, Vítor
Rocha, Miguel
KeywordsDeep learning
De novo molecular design
Recurrent neural network
Generative adversarial network
Generative model
Generative Adversarial Networks
Recurrent Neural Networks
Issue date26-Oct-2021
PublisherAmerican Chemical Society
JournalJournal of Chemical Information and Modeling
CitationSousa, Tiago; Correia, João; Pereira, Vítor; Rocha, Miguel, Generative deep learning for targeted compound design. Journal of Chemical Information and Modeling, 61(11), 5343-5361, 2021
Abstract(s)In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.
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Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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