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A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

https://doi.org/10.1002/int.22332
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Protein–ligand docking: Current status and future challenges
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Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
inferredcites →https://doi.org/10.1016/s0968-0004(98)01298-5openalex-snapshot
Iterated profile searches with PSI-BLAST—a tool for discovery in protein databases
inferredcites →https://doi.org/10.1016/s1359-6446(02)02288-2openalex-snapshot
Predicting ADME properties in silico: methods and models
inferredcites →https://doi.org/10.1021/ci100104jopenalex-snapshot
Estimation of ADME Properties with Substructure Pattern Recognition
inferredcites →https://doi.org/10.1021/ci600229zopenalex-snapshot
Structure-Based Identification of Small Molecule Binding Sites Using a Free Energy Model
inferredcites →https://doi.org/10.1038/nbt1273openalex-snapshot
Structure-based maximal affinity model predicts small-molecule druggability
inferredcites →https://doi.org/10.1038/nmeth.1432openalex-snapshot
Exploring the sequence determinants of amyloid structure using position-specific scoring matrices
inferredcites →https://doi.org/10.1038/nrd2199openalex-snapshot
How many drug targets are there?
inferredcites →https://doi.org/10.1038/s41598-019-44083-9openalex-snapshot
Evidence of large genetic influences on dog ownership in the Swedish Twin Registry has implications for understanding domestication and health associations
inferredcites →https://doi.org/10.1038/s41598-019-45814-8openalex-snapshot
Trader as a new optimization algorithm predicts drug-target interactions efficiently
inferredcites →https://doi.org/10.1039/c2mb00002dopenalex-snapshot
Drug–target interaction prediction by random walk on the heterogeneous network
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/10.1049/iet-ipr.2010.0471openalex-snapshot
Image thresholding framework based on two-dimensional digital fractional integration and Legendre moments’
inferredcites →https://doi.org/10.1073/pnas.84.13.4355openalex-snapshot
Profile analysis: detection of distantly related proteins.
inferredcites →https://doi.org/10.1093/bib/bbt056openalex-snapshot
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
inferredcites →https://doi.org/10.1093/bib/bbv066openalex-snapshot
Drug–target interaction prediction: databases, web servers and computational models
inferredcites →https://doi.org/10.1093/bib/bby002openalex-snapshot
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
inferredcites →https://doi.org/10.1093/bioinformatics/bti1141openalex-snapshot
A probabilistic model for mining implicit ‘chemical compound–gene’ relations from literature
inferredcites →https://doi.org/10.1093/bioinformatics/btn162openalex-snapshot
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
inferredcites →https://doi.org/10.1093/bioinformatics/btp039openalex-snapshot
Sequence-based prediction of protein interaction sites with an integrative method
inferredcites →https://doi.org/10.1093/bioinformatics/btq176openalex-snapshot
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
inferredcites →https://doi.org/10.1093/bioinformatics/bts360openalex-snapshot
Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization
inferredcites →https://doi.org/10.1093/bioinformatics/bts670openalex-snapshot
Drug–target interaction prediction by learning from local information and neighbors
inferredcites →https://doi.org/10.1093/bioinformatics/btt234openalex-snapshot
Predicting drug-target interactions using restricted Boltzmann machines
inferredcites →https://doi.org/10.1093/nar/30.1.412openalex-snapshot
TTD: Therapeutic Target Database
inferredcites →https://doi.org/10.1093/nar/gkh081openalex-snapshot
BRENDA, the enzyme database: updates and major new developments
inferredcites →https://doi.org/10.1093/nar/gkj102openalex-snapshot
From genomics to chemical genomics: new developments in KEGG
inferredcites →https://doi.org/10.1093/nar/gkm862openalex-snapshot
SuperTarget and Matador: resources for exploring drug-target relationships
inferredcites →https://doi.org/10.1093/nar/gkm958openalex-snapshot
DrugBank: a knowledgebase for drugs, drug actions and drug targets
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1093/nar/gks1189openalex-snapshot
Database resources of the National Center for Biotechnology Information
inferredcites →https://doi.org/10.1093/nar/gkt1207openalex-snapshot
STITCH 4: integration of protein–chemical interactions with user data
inferredcites →https://doi.org/10.1109/bibm47256.2019.8983181openalex-snapshot
HNEDTI: Prediction of drug-target interaction based on heterogeneous network embedding
inferredcites →https://doi.org/10.1109/iccabs.2017.8114299openalex-snapshot
Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction
inferredcites →https://doi.org/10.1109/siu.2016.7495942openalex-snapshot
Detection of copy move forgery using Legendre Moments
inferredcites →https://doi.org/10.1109/tnb.2013.2296050openalex-snapshot
A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition
inferredcites →https://doi.org/10.1109/tpami.2006.211openalex-snapshot
Rotation Forest: A New Classifier Ensemble Method
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1186/1752-0509-4-s2-s6openalex-snapshot
Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces
inferredcites →https://doi.org/10.1186/s12911-020-1052-0openalex-snapshot
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network
inferredcites →https://doi.org/10.1186/s12911-020-1101-8openalex-snapshot
Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure
inferredcites →https://doi.org/10.1364/josa.70.000920openalex-snapshot
Image analysis via the general theory of moments*
inferredcites →https://doi.org/10.1371/journal.pcbi.1002503openalex-snapshot
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
inferredcites →https://doi.org/10.1371/journal.pone.0062975openalex-snapshot
A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks
inferredcites →https://doi.org/10.2174/092986610789909403openalex-snapshot
Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor
inferredcites →https://doi.org/10.2174/1389203718666161114111656openalex-snapshot
RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information
inferredcites →https://doi.org/10.3389/fbioe.2020.00338openalex-snapshot
Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model
inferredcites →https://doi.org/10.3390/molecules22081366openalex-snapshot
Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
inferredcites →https://doi.org/10.3390/molecules24162999openalex-snapshot
Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Imbalance deep multi‐instance learning for predicting isoform–isoform interactions
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Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters
inferred← citeshttps://doi.org/10.1002/minf.202200102openalex-snapshot
A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions**
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Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning
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Detection of Drug-Drug Interactions Through Knowledge Graph Integrating Multi-attention with Capsule Network
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Drug–Target Interaction Prediction Based on Graph Neural Network and Recommendation System
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A Methodology for the Prediction of Drug Target Interaction Using CDK Descriptors
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Identifying Drug–Target Interactions Through a Combined Graph Attention Mechanism and Self-attention Sequence Embedding Model
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Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions
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DBCA-DTI: A dual-branch multimodal framework based on bidirectional adaptive gated cross-attention mechanism for drug-target interaction prediction
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Cheminformatic and Machine Learning based Approach for Predicting Drug Candidates
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AIGO-DTI: Predicting Drug–Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms
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MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism
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Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics
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DeepCNN-DTI: A Deep Learning Model for Detecting Drug-Target Interactions
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Drug-Target Interaction Prediction via Graph Auto-Encoder and Multi-Subspace Deep Neural Networks
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TransE-MDTI: A Few-Shot Transfer Learning Framework for Mutation-Sensitive Drug-Target Interaction Prediction
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Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation
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Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier
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Prediction algorithm for screening circRNA biomarker based on graph neural network
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LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions
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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning
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In silico Investigation of inhibitory characteristics of phytoconstituents from Moringa oleifera against SARS-CoV-2 viral proteins
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