| Download | - View final version: Multi-objective drug design based on graph-fragment molecular representation and deep evolutionary learning (PDF, 2.3 MiB)
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| DOI | Resolve DOI: https://doi.org/10.3389/fphar.2022.920747 |
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| Author | Search for: Mukaidaisi, Muhetaer; Search for: Vu, Andrew; Search for: Grantham, Karl; Search for: Tchagang, Alain1; Search for: Li, Yifeng |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Funder | Search for: Natural Resources Canada; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | drug design; multi-objective optimization; deep evolutionary learning; graph fragmentation; variational autoencoder; protein-ligand binding affinity |
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| Abstract | Drug discovery is a challenging process with a huge molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities. |
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| Publication date | 2022-07-04 |
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| Publisher | Frontiers Media |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | fb4d8958-6ece-41b7-9ce5-08db0c4a71c1 |
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| Record created | 2022-07-07 |
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| Record modified | 2022-07-08 |
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