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Article

De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

1
AI4U, 7321 Steinsel, Luxembourg
2
Department of Informatics, Ionian University, 49100 Corfu, Greece
*
Author to whom correspondence should be addressed.
Received: 29 September 2020 / Revised: 19 October 2020 / Accepted: 4 November 2020 / Published: 5 April 2021
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.
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Keywords: de novo drug design; synthetic molecule structure generation; deep learning de novo drug design; synthetic molecule structure generation; deep learning
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MDPI and ACS Style

Jacobs, I.; Maragoudakis, M. De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN. BioChem 2021, 1, 36-48. https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1010004

AMA Style

Jacobs I, Maragoudakis M. De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN. BioChem. 2021; 1(1):36-48. https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1010004

Chicago/Turabian Style

Jacobs, Ivan, and Manolis Maragoudakis. 2021. "De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN" BioChem 1, no. 1: 36-48. https://0-doi-org.brum.beds.ac.uk/10.3390/biochem1010004

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