Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an age...

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Detalles Bibliográficos
Autores principales: Kim, Hwanhee, Ko, Soohyun, Kim, Byung Ju, Ryu, Sung Jin, Ahn, Jaegyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733204/
https://www.ncbi.nlm.nih.gov/pubmed/36494855
http://dx.doi.org/10.1186/s13321-022-00666-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733204/
https://www.ncbi.nlm.nih.gov/pubmed/36494855
http://dx.doi.org/10.1186/s13321-022-00666-9

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