Accurate inference of potential drug–protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS. We ...
Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27%, is...
& Zhou, S. Improving compound–protein interaction prediction by building up highly credible negative samples. Bioinformatics 31, i221–i229 (2015). Article Google Scholar Gilson, M. K. et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems ...
Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids...
GitHub - masashitsubaki/CPI_prediction: This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins. DUD-E数据集: DUD-E: A Database of Useful (Docking) Decoys — Enhanced ...
GSL-DTI Graph Structure Learning Network for Drug-Target Interaction Prediction.pdf 1.2M · 百度网盘 摘要 动机:药物-靶标相互作用预测是预测药物分子与其靶蛋白之间是否存在相互作用的一个重要研究领域。它通过促进潜在候选药物的识别并加快整个过程,在药物发现和开发中发挥着关键作用。鉴于传统药物发现方法耗时、昂...
Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network PRODISTIN, a new computational method allowing the functional clustering of proteins on the basis of protein-protein interaction data. This method was used... C Brun,F Chevenet,D ...
Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very...
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for ...
DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications ...