Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning
In vitro library screening is a powerful approach to identify functional biopolymers, but only covers a fraction of possible sequences. Here, the authors use experimental in vitro selection results to train a conditional variational autoencoder machine learning model that generates biopolymers with...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-31955-4 |
| Summary: | In vitro library screening is a powerful approach to identify functional biopolymers, but only covers a fraction of possible sequences. Here, the authors use experimental in vitro selection results to train a conditional variational autoencoder machine learning model that generates biopolymers with no apparent sequence similarity to experimentally derived examples, but that nevertheless bind the target molecule with similar potent binding affinity. |
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| ISSN: | 2041-1723 |