Related references
Note: Only part of the references are listed.DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Yanyi Chu et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Identifying drug-target interactions based on graph convolutional network and deep neural network
Tianyi Zhao et al.
BRIEFINGS IN BIOINFORMATICS (2021)
MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
A. S. Rifaioglu et al.
BIOINFORMATICS (2021)
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
Yanyi Chu et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Explainable Deep Relational Networks for Predicting Compound- Protein Affinities and Contacts
Mostafa Karimi et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)
An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction
Jiajie Peng et al.
BRIEFINGS IN BIOINFORMATICS (2021)
A unified drug-target interaction prediction framework based on knowledge graph and recommendation system
Qing Ye et al.
NATURE COMMUNICATIONS (2021)
Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors
Arpan Mukherjee et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)
Mining Toxicity Information from Large Amounts of Toxicity Data
Zhenxing Wu et al.
JOURNAL OF MEDICINAL CHEMISTRY (2021)
ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction
Ziduo Yang et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)
Introducing block design in graph neural networks for molecular properties prediction
Yuquan Li et al.
CHEMICAL ENGINEERING JOURNAL (2021)
Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method
Ziduo Yang et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)
TrimNet: learning molecular representation from triplet messages for biomedicine
Pengyong Li et al.
BRIEFINGS IN BIOINFORMATICS (2021)
GraphDTA: predicting drug-target binding affinity with graph neural networks
Thin Nguyen et al.
BIOINFORMATICS (2021)
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
Zhaoping Xiong et al.
JOURNAL OF MEDICINAL CHEMISTRY (2020)
Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis
Gyoung S. Na et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Lifan Chen et al.
BIOINFORMATICS (2020)
Multi-view self-attention for interpretable drug-target interaction prediction
Brighter Agyemang et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2020)
An open source chemical structure curation pipeline using RDKit
A. Patricia Bento et al.
JOURNAL OF CHEMINFORMATICS (2020)
Drug-target affinity prediction using graph neural network and contact maps
Mingjian Jiang et al.
RSC ADVANCES (2020)
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
Ahmet Sureyya Rifaioglu et al.
CHEMICAL SCIENCE (2020)
Predicting drug-protein interaction using quasi-visual question answering system
Shuangjia Zheng et al.
NATURE MACHINE INTELLIGENCE (2020)
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Masashi Tsubaki et al.
BIOINFORMATICS (2019)
DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks
Mostafa Karimi et al.
BIOINFORMATICS (2019)
In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
Jochen Sieg et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Ingo Lee et al.
PLOS COMPUTATIONAL BIOLOGY (2019)
Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
Lieyang Chen et al.
PLOS ONE (2019)
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
Wen Torng et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
DeepDTA: deep drug-target binding affinity prediction
Hakime Ozturk et al.
BIOINFORMATICS (2018)
The rise of deep learning in drug discovery
Hongming Chen et al.
DRUG DISCOVERY TODAY (2018)
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu et al.
CHEMICAL SCIENCE (2018)
DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches
Rawan S. Olayan et al.
BIOINFORMATICS (2018)
Deep-Learning-Based Drug-Target Interaction Prediction
Ming Wen et al.
JOURNAL OF PROTEOME RESEARCH (2017)
Drug-target interaction prediction using ensemble learning and dimensionality reduction
Ali Ezzat et al.
METHODS (2017)
SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines
Tong He et al.
JOURNAL OF CHEMINFORMATICS (2017)
Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
Eelke B. Lenselink et al.
JOURNAL OF CHEMINFORMATICS (2017)
Low Data Drug Discovery with One-Shot Learning
Han Altae-Tran et al.
ACS CENTRAL SCIENCE (2017)
A kernel matrix dimension reduction method for predicting drug-target interaction
Qifan Kuang et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2017)
BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology
Michael K. Gilson et al.
NUCLEIC ACIDS RESEARCH (2016)
PubChem Substance and Compound databases
Sunghwan Kim et al.
NUCLEIC ACIDS RESEARCH (2016)
Target identification for biologically active small molecules using chemical biology approaches
Heesu Lee et al.
ARCHIVES OF PHARMACAL RESEARCH (2016)
Identifying compound efficacy targets in phenotypic drug discovery
Markus Schirle et al.
DRUG DISCOVERY TODAY (2016)
Activity, assay and target data curation and quality in the ChEMBL database
George Papadatos et al.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2015)
Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects
Isidro Cortes-Ciriano et al.
MEDCHEMCOMM (2015)
Toward more realistic drug-target interaction predictions
Tapio Pahikkala et al.
BRIEFINGS IN BIOINFORMATICS (2015)
Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
Jing Tang et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2014)
Some case studies on application of rm2 metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data
Kunal Roy et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2013)
STRUCTURE-BASED METHODS FOR COMPUTATIONAL PROTEIN FUNCTIONAL SITE PREDICTION
B. K. C. Dukka
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2013)
Comprehensive analysis of kinase inhibitor selectivity
Mindy I. Davis et al.
NATURE BIOTECHNOLOGY (2011)
Navigating the kinome
James T. Metz et al.
NATURE CHEMICAL BIOLOGY (2011)
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
Gerard J. P. van Westen et al.
MEDCHEMCOMM (2011)
Glucolipotoxicity of the pancreatic beta cell
Vincent Poitout et al.
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR AND CELL BIOLOGY OF LIPIDS (2010)
Prediction of drug-target interaction networks from the integration of chemical and genomic spaces
Yoshihiro Yamanishi et al.
BIOINFORMATICS (2008)
Concordance probability and discriminatory power in proportional hazards regression
M Gönen et al.
BIOMETRIKA (2005)
Derivation and validation of toxicophores for mutagenicity prediction
J Kazius et al.
JOURNAL OF MEDICINAL CHEMISTRY (2005)
Molecular dynamics simulations of biomolecules
M Karplus et al.
NATURE STRUCTURAL BIOLOGY (2002)
Share Paper
