Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly.

ABSTRACT: 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

SUBMITTER: Seo S 

PROVIDER: S-EPMC10015872 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature