Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of drugs with targeting metabolic pathways in the organisms can provide a more complete understanding of the metabolic effects of a drug and help to identify potential drug-drug interactions.

Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search.

Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search.

DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties.

Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions within the pathway modules.

FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding.

Unlabelled: Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately.

A novel hybrid framework for metabolic pathways prediction based on the graph attention network.

Background: Making clear what kinds of metabolic pathways a drug compound involves in can help researchers understand how the drug is absorbed, distributed, metabolized, and excreted. The characteristics of a compound such as structure, composition and so on directly determine the metabolic pathways it participates in.

Methods: We developed a novel hybrid framework based on the graph attention network (GAT) to predict the metabolic pathway classes that a compound involves in, named HFGAT, by making use of its global and local characteristics.

DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database.

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.

Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology.