Human intestinal absorption (HIA) is a crucial indicator for measuring the oral bioavailability of drugs.This study aims to use artificial intelligence methods to predict and evaluate the HIA of drugs in the early stages of drug discovery, thus accelerating the drug discovery process and reducing costs.This study used MOE''s 2D, 3D descriptors, and ECFP4 (extended connectivity fingerprints) to characterize the molecules and established eight models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN).The results showed that the SVM model constructed using a combination of 2D, 3D descriptors and ECFP4 fingerprints was the optimal model according to comprehensive evaluation of various evaluation indicators.The area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient, and Kappa coefficient of the optimal model were 0.94, 0.75, and 0.74, respectively.In conclusion, this study established a robust and generalizable machine learning model for predicting HIA properties, which can provide guidance for early molecular screening and the study of pharmacokinetic properties of drugs.

There are more than 150 types of chemical modifications in RNA, mainly methylation, which are widely distributed in all kinds of RNA, including messenger RNA, transfer RNA, ribosomal RNA, non-coding small RNA and long non-coding RNA. In recent years, the identification of RNA methylation modification enzymes and the development of high-throughput sequencing technology at transcriptome level laid a foundation for revealing the expression and function of genes regulated by chemical modification of RNA. In this review, the most recent advances of RNA methylation, especially N6-methyladenosine (m
Adenosine/metabolism* ; Hematopoiesis ; Humans ; Methylation ; RNA/metabolism*