On the public citation graph
GraphDTA: Predicting drug–target binding affinity with graph neural networks
0 signed · 140 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10822-016-9938-8openalex-snapshot
Molecular graph convolutions: moving beyond fingerprints
inferredcites →https://doi.org/10.1007/s12539-019-00327-wopenalex-snapshot
An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
inferredcites →https://doi.org/10.1016/j.copbio.2011.11.010openalex-snapshot
Drug discovery in the age of systems biology: the rise of computational approaches for data integration
inferredcites →https://doi.org/10.1021/acs.jcim.9b00628openalex-snapshot
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
inferredcites →https://doi.org/10.1021/ci00057a005openalex-snapshot
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
inferredcites →https://doi.org/10.1021/ci034233wopenalex-snapshot
Feature Selection for Descriptor Based Classification Models. 2. Human Intestinal Absorption (HIA)
inferredcites →https://doi.org/10.1021/ci100369fopenalex-snapshot
A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing
inferredcites →https://doi.org/10.1021/ci400709dopenalex-snapshot
Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
inferredcites →https://doi.org/10.1021/jm5006429openalex-snapshot
Maximizing Diversity from a Kinase Screen: Identification of Novel and Selective pan-Trk Inhibitors for Chronic Pain
inferredcites →https://doi.org/10.1038/nbt.1990openalex-snapshot
Comprehensive analysis of kinase inhibitor selectivity
inferredcites →https://doi.org/10.1038/nm.3595openalex-snapshot
Overcoming Drug Development Bottlenecks With Repurposing: Old drugs learn new tricks
inferredcites →https://doi.org/10.1038/nm.4306openalex-snapshot
The Drug Repurposing Hub: a next-generation drug library and information resource
inferredcites →https://doi.org/10.1038/nrd1468openalex-snapshot
Drug repositioning: identifying and developing new uses for existing drugs
inferredcites →https://doi.org/10.1038/nrd2593openalex-snapshot
Pharmacogenetics in drug discovery and development: a translational perspective
inferredcites →https://doi.org/10.1038/nrd4507openalex-snapshot
New drugs cost US$2.6 billion to develop
inferredcites →https://doi.org/10.1038/nrd773openalex-snapshot
Protein kinases — the major drug targets of the twenty-first century?
inferredcites →https://doi.org/10.1038/s41598-018-25440-6openalex-snapshot
Linguistic measures of chemical diversity and the “keywords” of molecular collections
inferredcites →https://doi.org/10.1039/c8sc00148kopenalex-snapshot
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
inferredcites →https://doi.org/10.1093/bib/bbaa205openalex-snapshot
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
inferredcites →https://doi.org/10.1093/bib/bbz042openalex-snapshot
Graph convolutional networks for computational drug development and discovery
inferredcites →https://doi.org/10.1093/bib/bbz152openalex-snapshot
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
inferredcites →https://doi.org/10.1093/bioinformatics/bty277openalex-snapshot
Learning with multiple pairwise kernels for drug bioactivity prediction
inferredcites →https://doi.org/10.1093/bioinformatics/bty593openalex-snapshot
DeepDTA: deep drug–target binding affinity prediction
inferredcites →https://doi.org/10.1093/nar/gkp885openalex-snapshot
PROSITE, a protein domain database for functional characterization and annotation
inferredcites →https://doi.org/10.1093/nar/gkr320openalex-snapshot
ChemMine tools: an online service for analyzing and clustering small molecules
inferredcites →https://doi.org/10.1101/2020.03.22.002386openalex-snapshot
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
inferredcites →https://doi.org/10.1109/tkde.2005.127openalex-snapshot
Frequent substructure-based approaches for classifying chemical compounds
inferredcites →https://doi.org/10.1126/science.1075762openalex-snapshot
The Protein Kinase Complement of the Human Genome
inferredcites →https://doi.org/10.1126/science.1095920openalex-snapshot
Protein Kinase Inhibitors: Insights into Drug Design from Structure
inferredcites →https://doi.org/10.1145/3219819.3219947openalex-snapshot
Large-Scale Learnable Graph Convolutional Networks
inferredcites →https://doi.org/10.1186/s12859-018-2523-5openalex-snapshot
Convolutional neural network based on SMILES representation of compounds for detecting chemical motif
inferredcites →https://doi.org/10.1186/s12864-020-6652-7openalex-snapshot
Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome
inferredcites →https://doi.org/10.1186/s13321-017-0209-zopenalex-snapshot
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
inferredcites →https://doi.org/10.1371/journal.pcbi.1005678openalex-snapshot
Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
inferredcites →https://doi.org/10.24963/ijcai.2018/468openalex-snapshot
Interpretable Drug Target Prediction Using Deep Neural Representation
inferredcites →https://doi.org/10.3390/ijms20143389openalex-snapshot
Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1807.09741openalex-snapshot
PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-68154-8_71openalex-snapshot
Graph Neural Networks in Cheminformatics
inferred← citeshttps://doi.org/10.1007/978-981-19-4549-6_6openalex-snapshot
Predicting Drug-Target Interactions Binding Affinity by Using Dual Updating Multi-task Learning
inferred← citeshttps://doi.org/10.1007/978-981-97-5692-6_17openalex-snapshot
HyperCPI: A Novel Method Based on Hypergraph for Compound Protein Interaction Prediction with Good Generalization Ability
inferred← citeshttps://doi.org/10.1007/978-981-99-1230-8_24openalex-snapshot
Graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures
inferred← citeshttps://doi.org/10.1007/978-981-99-4749-2_66openalex-snapshot
Drug-Target Affinity Prediction Based on Self-attention Graph Pooling and Mutual Interaction Neural Network
inferred← citeshttps://doi.org/10.1007/s10489-024-05936-7openalex-snapshot
MultiGranDTI: an explainable multi-granularity representation framework for drug-target interaction prediction
inferred← citeshttps://doi.org/10.1007/s11030-021-10256-wopenalex-snapshot
Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
inferred← citeshttps://doi.org/10.1007/s11427-022-2239-yopenalex-snapshot
PanGu Drug Model: learn a molecule like a human
inferred← citeshttps://doi.org/10.1007/s11704-022-2163-9openalex-snapshot
FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2024.108310openalex-snapshot
Improving binding affinity prediction by emphasizing local features of drug and protein
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106145openalex-snapshot
GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107131openalex-snapshot
StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108376openalex-snapshot
G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.csbj.2021.03.004openalex-snapshot
A review on compound-protein interaction prediction methods: Data, format, representation and model
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115525openalex-snapshot
EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction
inferred← citeshttps://doi.org/10.1016/j.jmgm.2022.108283openalex-snapshot
A deep learning method for predicting molecular properties and compound-protein interactions
inferred← citeshttps://doi.org/10.1016/j.sbi.2021.102327openalex-snapshot
Graph neural network approaches for drug-target interactions
inferred← citeshttps://doi.org/10.1016/j.ymeth.2022.09.006openalex-snapshot
MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1017/9781108924184.003openalex-snapshot
Deep Learning on Graphs: An Introduction
inferred← citeshttps://doi.org/10.1017/9781108924184.006openalex-snapshot
Foundations of Deep Learning
inferred← citeshttps://doi.org/10.1017/9781108924184.010openalex-snapshot
Robust Graph Neural Networks
inferred← citeshttps://doi.org/10.1017/9781108924184.011openalex-snapshot
Scalable Graph Neural Networks
inferred← citeshttps://doi.org/10.1017/9781108924184.012openalex-snapshot
Graph Neural Networks for Complex Graphs
inferred← citeshttps://doi.org/10.1017/9781108924184.013openalex-snapshot
Beyond GNNs: More Deep Models on Graphs
inferred← citeshttps://doi.org/10.1017/9781108924184.015openalex-snapshot
Graph Neural Networks in Natural Language Processing
inferred← citeshttps://doi.org/10.1017/9781108924184.016openalex-snapshot
Graph Neural Networks in Computer Vision
inferred← citeshttps://doi.org/10.1017/9781108924184.017openalex-snapshot
Graph Neural Networks in Data Mining
inferred← citeshttps://doi.org/10.1017/9781108924184.018openalex-snapshot
Graph Neural Networks in Biochemistry and Health Care
inferred← citeshttps://doi.org/10.1017/9781108924184.020openalex-snapshot
Advanced Topics in Graph Neural Networks
inferred← citeshttps://doi.org/10.1017/9781108924184.021openalex-snapshot
Advanced Applications in Graph Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01057openalex-snapshot
DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features
inferred← citeshttps://doi.org/10.1021/acsomega.2c06653openalex-snapshot
Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1038/s41598-022-08787-9openalex-snapshot
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
inferred← citeshttps://doi.org/10.1038/s42256-025-01151-2openalex-snapshot
Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology
inferred← citeshttps://doi.org/10.1042/etls20210225openalex-snapshot
Graph representation learning for structural proteomics.
inferred← citeshttps://doi.org/10.1088/1742-6596/2219/1/012008openalex-snapshot
GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM
inferred← citeshttps://doi.org/10.1093/bib/bbaa043openalex-snapshot
Application of deep learning methods in biological networks
inferred← citeshttps://doi.org/10.1093/bib/bbab117openalex-snapshot
Deep drug-target binding affinity prediction with multiple attention blocks
inferred← citeshttps://doi.org/10.1093/bib/bbac350openalex-snapshot
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery
inferred← citeshttps://doi.org/10.1093/bib/bbac533openalex-snapshot
SAM-DTA: a sequence-agnostic model for drug–target binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab548openalex-snapshot
DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf117openalex-snapshot
H2GnnDTI: hierarchical heterogeneous graph neural networks for drug–target interaction prediction
inferred← citeshttps://doi.org/10.1101/2020.08.04.236729openalex-snapshot
Genome-wide Prediction of Small Molecule Binding to Remote Orphan Proteins Using Distilled Sequence Alignment Embedding
inferred← citeshttps://doi.org/10.1101/2020.08.27.270967openalex-snapshot
Learning Distance-Dependent Motif Interactions: An Explicitly Interpretable Neural Model of Genomic Events
inferred← citeshttps://doi.org/10.1101/2020.10.08.332346openalex-snapshot
A deep learning framework for elucidating whole-genome chemical interaction space
inferred← citeshttps://doi.org/10.1101/2021.06.17.448780openalex-snapshot
X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction
inferred← citeshttps://doi.org/10.1101/2021.09.30.462610openalex-snapshot
Drug-Target Binding Affinity Prediction Using Transformers
inferred← citeshttps://doi.org/10.1101/2023.01.27.525827openalex-snapshot
DeepERA: deep learning enables comprehensive identification of drug-target interactions via embedding of heterogeneous data
inferred← citeshttps://doi.org/10.1101/2023.05.25.23290531openalex-snapshot
Advancing Efficacy Prediction for EHR-based Emulated Trials in Repurposing Heart Failure Therapies
inferred← citeshttps://doi.org/10.1101/2023.08.01.551396openalex-snapshot
A versatile framework for drug-target interaction prediction by considering domain specific features
inferred← citeshttps://doi.org/10.1109/access.2020.2968535openalex-snapshot
Molecular Property Prediction Based on a Multichannel Substructure Graph
inferred← citeshttps://doi.org/10.1109/access.2020.3022850openalex-snapshot
Enhanced Graph Isomorphism Network for Molecular ADMET Properties Prediction
inferred← citeshttps://doi.org/10.1109/access.2020.3024238openalex-snapshot
DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing
inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983254openalex-snapshot
Drug Target Interaction Prediction using Multi-task Learning and Co-attention
inferred← citeshttps://doi.org/10.1109/bibm49941.2020.9313151openalex-snapshot
Repositioning Molecules of Chinese Medicine to Targets of SARS-Cov-2 by Deep Learning Method
inferred← citeshttps://doi.org/10.1109/ictai50040.2020.00060openalex-snapshot
DTIGCCN: Prediction of drug-target interactions based on GCN and CNN
inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9533382openalex-snapshot
Drug-drug Interaction Prediction with Common Structural Patterns
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3060430openalex-snapshot
Graph Convolutional Networks for Drug Response Prediction
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3077905openalex-snapshot
Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3103966openalex-snapshot
DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3206888openalex-snapshot
Graph Transformer for Drug Response Prediction
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3225296openalex-snapshot
CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network
inferred← citeshttps://doi.org/10.1109/tcbbio.2025.3549588openalex-snapshot
MIN: Multi-Channel Interaction Network for Drug-Target Interaction With Protein Distillation
inferred← citeshttps://doi.org/10.1111/exsy.13683openalex-snapshot
Optimizing hybrid deep learning models for drug‐target interaction prediction: A comparative analysis of evolutionary algorithms
inferred← citeshttps://doi.org/10.1145/3447548.3467286openalex-snapshot
Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery
inferred← citeshttps://doi.org/10.1145/3543377.3543399openalex-snapshot
A Graph Convolution-Transformer Neural Network for Drug-Target Interaction Prediction
inferred← citeshttps://doi.org/10.1145/3608164.3608198openalex-snapshot
Improving Prediction of Drug-Target Binding Affinity using Attention Mechanism and Bi-Directional Long Short-Term Memory
inferred← citeshttps://doi.org/10.1155/2022/9044793openalex-snapshot
Application of DNA‐Binding Protein Prediction Based on Graph Convolutional Network and Contact Map
inferred← citeshttps://doi.org/10.1186/s13321-020-00435-6openalex-snapshot
kGCN: a graph-based deep learning framework for chemical structures
inferred← citeshttps://doi.org/10.1186/s13321-026-01167-9openalex-snapshot
LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions
inferred← citeshttps://doi.org/10.12677/airr.2024.132034openalex-snapshot
CT-CPI: Deep Learning Model of Compound-Protein Interaction Based on Integrated CNN Module and Transformer
inferred← citeshttps://doi.org/10.1371/journal.pone.0249404openalex-snapshot
Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities
inferred← citeshttps://doi.org/10.1371/journal.pone.0258628openalex-snapshot
Predicting compound-protein interaction using hierarchical graph convolutional networks
inferred← citeshttps://doi.org/10.15302/j-qb-022-0320openalex-snapshot
DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction
inferred← citeshttps://doi.org/10.24124/2024/59584openalex-snapshot
Transformer models for protein-guided drug compound generation: A comparison of amino acid sequences, pre-trained protein embeddings, SMILES, and SELFIES
inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-gk3n6openalex-snapshot
CarbonAI, A Non-Docking Deep learning based small molecule virtual screening platform
inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-gk3n6-v2openalex-snapshot
CarbonAI, A Non-Docking Deep learning based small molecule virtual screening platform
inferred← citeshttps://doi.org/10.3389/fbinf.2023.1193025openalex-snapshot
Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs
inferred← citeshttps://doi.org/10.3389/fbioe.2020.00267openalex-snapshot
Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction
inferred← citeshttps://doi.org/10.3389/fgene.2021.680117openalex-snapshot
Application of Machine Learning for Drug–Target Interaction Prediction
inferred← citeshttps://doi.org/10.3390/biom11050643openalex-snapshot
CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.3390/biom11121783openalex-snapshot
EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
inferred← citeshttps://doi.org/10.3390/biom12070932openalex-snapshot
Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
inferred← citeshttps://doi.org/10.3390/ijms22168993openalex-snapshot
SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
inferred← citeshttps://doi.org/10.3390/molecules27165114openalex-snapshot
Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks
inferred← citeshttps://doi.org/10.3390/pharmaceutics12090879openalex-snapshot
Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks
inferred← citeshttps://doi.org/10.3390/pharmaceutics13111906openalex-snapshot
Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
inferred← citeshttps://doi.org/10.5121/csit.2022.1207openalex-snapshot
Data Mining and Machine Learning
inferred← citeshttps://doi.org/10.5121/csit.2022.120703openalex-snapshot
GDGRU-DTA: Predicting Drug-Target Binding Affinity based on GNN and Double GRU
inferred← citeshttps://doi.org/10.5121/mlaij.2022.9201openalex-snapshot
DSAGLSTM-DTA: Prediction of Drug-Target Affinity using Dual Self-Attention and LSTM
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.