Cargando…

Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly

Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosyn...

Descripción completa

Detalles Bibliográficos
Autores principales: Seo, Seonghwan, Lim, Jaechang, Kim, Woo Youn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015872/
https://www.ncbi.nlm.nih.gov/pubmed/36596675
http://dx.doi.org/10.1002/advs.202206674
Descripción
Sumario:Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2.