Dataset Information

In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network.

ABSTRACT: UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fate of UGT enzymes, in silico methods have been developed to predict the UGT-mediated metabolism of drug-like molecules. We developed consensus models with the combination of machine learning (ML) and graph neural network (GNN) methods to predict if a drug-like molecule is a potential UGT substrate, and then we applied the Weisfeiler-Lehman Network (WLN) model to identify the sites of metabolism (SOMs) of UGT-catalyzed substrates. For the substrate model, the accuracy of the single substrate prediction model on the test set could reach to 0.835. Compared with the single estimators, the consensus models are more stable and have better generalization ability, and the accuracy on the test set reached to 0.851. For the SOM model, the top-1 accuracy of the SOM model on the test set reached to 0.898, outperforming existing works. Thus, in this study, we proposed a computational framework, named Meta-UGT, which would provide a useful tool for the prediction and optimization of metabolic profiles and drug design.

SUBMITTER: Huang M

PROVIDER: S-EPMC9270812 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Dataset Information

In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets