Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation.

ABSTRACT: Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. With the development of deep learning techniques for the de novo generation of molecules, also known as inverse molecular design, the increasingly widespread application of various types of deep learning algorithms has led to revolutionary changes in de novo molecular generation research. In particular, the emergence of a novel natural language processing (NLP) architecture called the transformer has improved the state-of-the-art performance of existing AI technologies. In this study, we modified one top-performing molecular generation model on the basis of the generative pretraining transformer (GPT) architecture in three directions. Moreover, we prop

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11799282 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature