Hnet-DTI: Incorporating heterogeneous information network for drug-target interaction prediction

Download

HIBIT22_paper_123.pdf

Date

2022-10

Author

Özsarı , Gökhan
Rifaioğlu , Ahmet Süreyya
Doğan , Tunca
Atalay, Volkan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

144
views

73
downloads

Identifying drug-target interactions (DTIs) is crucial in drug discovery. In silico prediction of interactions between drugs and target proteins is needed to accelerate the drug discovery process. Although many DTI prediction methods have been proposed in recent years, there is still room for improvement. This study proposes a graph neural network-based method to exploit the heterogeneous information network for drug-target interaction prediction. The study includes two major contributions: Heterogeneous information network: We first constructed a heterogeneous information network that includes two types of nodes as nodes and three types of edges. The network includes 15 291 protein nodes, 3 444 drug nodes, 475 850 protein-protein interaction edges, 2 296 964 drug-drug interaction edges and 6 018 drug-protein interaction edges. DTI prediction method: We applied a three-layer graph convolution neural network (GCN) to learn the embeddings of drugs and proteins by utilizing neighborhood information in the network. GCNs were employed for each node type to aggregate neighborhood information. The final embeddings were a concatenation of embeddings from three layers of GCN. Finally, the embeddings of drugs and proteins were multiplied with a learnable weight matrix to predict the drug-target interactions. We evaluated the performance of the proposed method by using a 10-fold cross-validation technique and compared the proposed method baseline classifiers: random forest (RF) and support vector machines (SVM). The results indicate that the proposed method outperforms the baseline classifiers by reaching a 0.898 AUROC score.

URI

https://hibit2022.ims.metu.edu.tr/
https://hdl.handle.net/11511/101942

Conference Name

The International Symposium on Health Informatics and Bioinformatics

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations Rifaioğlu, Ahmet Süreyya; Nalbat, Esra; Atalay, Mehmet Volkan (2020-03-07) The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantage...
MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery Rifaioglu, A.S.; Atalay, R. Cetin; Kahraman, Deniz Cansen; DOĞAN, TUNCA; Martin, M.; Atalay, Mehmet Volkan (2021-03-01) Motivation: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental bioactivity screening efforts require the aid of computational approaches. Although deep learning models have been successful in predicting bioactive compounds, effective and comprehensive featurization of proteins, to be given as input to deep neural networks, r...
Predicting the binding affinities of drug-protein interaction by analyzing the images of binding sites Erdaş, Özlem; Alpaslan, Ferda Nur; Büyükbingöl, Erdem; Department of Computer Engineering (2013) Analysis of protein-ligand interactions plays an important role in designing safe and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have been found useful in drug design, which utilize intelligent techniques to predict unknown protein-ligand interactions by learning from specific properties of known protein-ligand interactions. The aim of this thesis is to propose a novel computational model, Compressed Images for Affinity Prediction (CIFAP), to predict ...
Dual-adjuvant effect of pH-sensitive liposomes loaded with STING and TLR9 agonists regress tumor development by enhancing Th1 immune response Kocabas, Banu Bayyurt; Almacioglu, Kubra; Bulut, Esin Alpdundar; Gucluler, Gozde; Tincer, Gizem; Bayik, Defne; Gürsel, Mayda; GÜRSEL, İHSAN (Elsevier BV, 2020-12-01) Nucleic acid-based pattern recognition receptor agonists are effective adjuvants and immunotherapeutic agents. Rather than single applications, ligand combinations could synergistically potentiate immune responses by elevating cytokine and chemokine production via triggering multiple signaling pathways. However, short half-lives of such labile ligands due to nuclease attack and limited cellular uptake due to their structure significantly hamper their in vivo performances. More importantly, simultaneous deli...
RANKPCSF: A DISEASE MODULE IDENTIFICATION METHOD BY INTEGRATING NETWORK PROPAGATION WITH PRIZE-COLLECTING STEINER FOREST Eskin, Arda; Otlu Sarıtaş, Burçak; Tunçbağ, Nurcan; Department of Bioinformatics (2022-9-2) Identification of disease modules may lead to a better understanding on the progression of diseases, finding more accurate biomarkers, and drug targets. In this study, we develop a hybrid method – RANKPCSF- combining the strength of Steiner tree and diffusion approaches and release a new network reconstruction approach. RANKPCSF is capable to integrate multi-omic data (including phosphoproteomic and transcriptomic) with a reference interactome to unveil the optimal disease-associated network. We have compar...

Citation Formats

G. Özsarı, A. S. Rifaioğlu, T. Doğan, and V. Atalay, “Hnet-DTI: Incorporating heterogeneous information network for drug-target interaction prediction,” Erdemli, Mersin, TÜRKİYE, 2022, p. 3123, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr/.