AUTHOR=Liu Jianping , Lei Xiujuan , Ji Chunyan , Pan Yi TITLE=Fragment-pair based drug molecule solubility prediction through attention mechanism JOURNAL=Frontiers in Pharmacology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1255181 DOI=10.3389/fphar.2023.1255181 ISSN=1663-9812 ABSTRACT=

The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness.