Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure–Potency Fingerprint

Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure–activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed mo...

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Detalles Bibliográficos
Autores principales: Janela, Tiago, Takeuchi, Kosuke, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953226/
https://www.ncbi.nlm.nih.gov/pubmed/36830761
http://dx.doi.org/10.3390/biom13020393

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953226/
https://www.ncbi.nlm.nih.gov/pubmed/36830761
http://dx.doi.org/10.3390/biom13020393

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