Machine learning-driven protein engineering: a case study in computational drug discovery.
DNA
DNA library synthesis
ML‐driven drug discovery
biology computing
computational drug discovery
deep learning
directed evolution
drugs
generation sequencing
great expectation
high‐quality datasets
high‐throughput display
learning (artificial intelligence)
learnings
machine learning‐driven protein engineering
molecular biophysics
multiple important protein characteristics
optimisation
proteins
selection data generation
significant efficiency gains
silico models
ultra‐high throughput selections
Journal
Engineering biology
ISSN: 2398-6182
Titre abrégé: Eng Biol
Pays: United States
ID NLM: 9918539388906676
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
received:
13
12
2019
revised:
18
02
2020
accepted:
24
02
2020
entrez:
27
3
2023
pubmed:
16
3
2020
medline:
16
3
2020
Statut:
epublish
Résumé
Research and development in drug discovery will need to find significant efficiency gains if the industry is to continue generating novel drugs. There is great expectation for machine learning (ML) to provide this boost in R&D productivity, but to harness the full potential of ML, the generation of new, high-quality datasets will be necessary. Here, the authors present a platform that combines high-throughput display and selection data generation with ML. More specifically, deep learning is used to inform the directed evolution of novel biotherapeutics using DNA library synthesis, ultra-high throughput selections, and next generation sequencing. By combining the learnings of multiple
Identifiants
pubmed: 36970228
doi: 10.1049/enb.2019.0019
pii: ENB2BF00047
pmc: PMC9996701
doi:
Types de publication
Journal Article
Langues
eng
Pagination
7-9Informations de copyright
© 2020 The Institution of Engineering and Technology.
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