PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.
AlphaFold
Dark genome
Drug target
Druggability
Interpretable
Machine learning
Neural network
Protein
Protein pocket
Proteome
Journal
Journal of cheminformatics
ISSN: 1758-2946
Titre abrégé: J Cheminform
Pays: England
ID NLM: 101516718
Informations de publication
Date de publication:
19 Jul 2023
19 Jul 2023
Historique:
received:
23
03
2023
accepted:
10
07
2023
medline:
20
7
2023
pubmed:
20
7
2023
entrez:
19
7
2023
Statut:
epublish
Résumé
The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.
Identifiants
pubmed: 37468968
doi: 10.1186/s13321-023-00735-7
pii: 10.1186/s13321-023-00735-7
pmc: PMC10354961
doi:
Types de publication
Letter
Langues
eng
Pagination
64Informations de copyright
© 2023. The Author(s).
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