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

Abstract

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 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

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

“…If only a single-task model is used for modelling, data association information between subtasks would be lost. Therefore, we developed the multi-task FP-GNN framework to prevent data loss from subtasks, which was then successfully used to accurately predict inhibitors of four poly ADP-ribose polymerase (PARP) isoforms ( Ai et al, 2022 ). In this study, we continue to extend the application of the multi-task FP-GNN method in predicting the inhibitory activity of molecules against five CYPs (1A2, 2C9, 2C19, 2D6, and 3A4, Figure 1 ).…”

VDDB: A comprehensive resource and machine learning tool for antiviral drug discovery

“…FP‐GNN is a novel DL architecture for molecular property prediction, which combined and simultaneously studied the information from molecular graphs and molecular fingerprints 52–54 . The evaluation outcomes illustrated that FP‐GNN was a competitive DL algorithm for the molecular property prediction tasks.…”

“…FP-GNN is a novel DL architecture for molecular property prediction, which combined and simultaneously studied the information from molecular graphs and molecular fingerprints. [52][53][54] The evaluation outcomes illustrated that FP-GNN was a competitive DL algorithm for the molecular property prediction tasks. To achieve the best FP-GNN model, six hyperparameters are chosen to optimize by using the Hyperopt Pythonpackage 55 : the dropout rate of GAN, the number of multihead attentions, the hidden size of attentions, the hidden size and dropout rate of FPN, and the ratio of GNN in FP-GNN.…”

In vitro biological studies and computational prediction-based analyses of pyrazolo[1,5-a]pyrimidine derivatives

“…Five CYPs (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) are responsible for metabolizing most approved drugs, and drug interactions involving CYPs can lead to premature termination of drug development and withdrawal from the market. 98 From Table 5, we found that the two pyrazolo[1,5-a]pyrimidines, 12a and 12b, are noninhibitors of the CYP2D6 enzyme but inhibitors of other enzymes CYP1A2, CYP2C19, CYP2C9, and CYP3A4.…”