DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction.

Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market.

Targeting the PI3K/AKT/mTOR Signaling Pathway in the Treatment of Human Diseases: Current Status, Trends, and Solutions.

The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway is one of the most important intracellular pathways involved in cell proliferation, growth, differentiation, and survival. Therefore, this route is a prospective biological target for treating various human diseases, such as tumors, neurodegenerative diseases, pulmonary fibrosis, and diabetes. An increasing number of clinical studies emphasize the necessity of developing novel molecules targeting the PI3K/AKT/mTOR pathway.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors.

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.

Design, synthesis and biological evaluation of novel hybrids targeting mTOR and HDACs for potential treatment of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major contributor to global cancer incidence and mortality. Many pathways are involved in the development of HCC and various proteins including mTOR and HDACs have been identified as potential drug targets for HCC treatment. In the present study, two series of novel hybrid molecules targeting mTOR and HDACs were designed and synthesized based on parent inhibitors (MLN0128 and PP121 for mTOR, SAHA for HDACs) by using a fusion-type molecular hybridization strategy.