DeepSA: a deep-learning driven predictor of compound synthesis accessibility.

ABSTRACT: With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic

SUBMITTER: Wang S 

PROVIDER: S-EPMC10621138 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature