Predicting circRNA-Drug Sensitivity Using Integrated Hybrid Graph and Molecular Features.

Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network

Abstract Motivation Understanding how small molecules modulate cellular states remains a critical challenge in drug discovery. The advent of perturbation transcriptomics offers new avenues for elucidating drug-target interactions by capturing cellular transcriptional responses to perturbations. Results In this study, we propose BioHSNet, a biological function-guided hypergraph siamese network for inferring drug-target interactions from perturbation transcriptomics. BioHSNet utilizes hyperedge representations of functionally grouped gene expression to capture higher-order functional relationships, and integrates compound structural information into the model to bridge chemical structure and functional response. Experimental results demonstrate that BioHSNet outperforms other transcriptome-based methods on the Broad Institute’s L1000 datasets, particularly in cold start scenarios. The case study further demonstrates its practical utility for target prediction and drug screening. Availability and Implementation The source code is available at https://github.com/Zxinyizhang/BioHSNet.

Drug sensitivity prediction across cancer types using graph isomorphism networks and biological pathway features: A dual-branch deep learning approach

An innovative dual-branch approach based on Graph Isomorphism Network drug representations coupled with a Multilayer Perceptron (MLP) for 50-dimensional ssGSEA pathway activities calculated from CCLE gene expression is proposed, proving the importance of biological features in the two-branch model.

Molecular Fragment-Based Graph Isomorphism Networks for Interpretable Prediction of Synergistic Drug Combinations

Results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.

IHLO-DTI: Drug-Target Interaction Prediction Based on Improved Hypergraph Neural Network and Laplacian Matrix Optimization

IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.

Predicting miRNA-disease associations based on adaptive neighborhood propagation and feature spatial recombination.

A novel GNN framework, APKAGN, designed for predicting miRNA-disease associations significantly enhances the accuracy of MDAs prediction, offering a powerful tool for investigating disease mechanisms and identifying biomarkers.

Heterogeneous dual-channel and interpretable graph representation learning with global virtual nodes for microRNA-mediated drug sensitivity prediction.