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Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

https://doi.org/10.1093/bib/bbad014
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Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

7 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 56 checked references that resolve
resolves10.1016/j.cbpa.2012.12.022
Target deconvolution techniques in modern phenotypic profiling
resolves10.1038/s41598-017-04264-w
Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
resolves10.1016/j.tibtech.2018.06.008
Chemoproteomics and Chemical Probes for Target Discovery
resolves10.1016/j.cell.2017.08.051
Chemical Proteomics Identifies Druggable Vulnerabilities in a Genetically Defined Cancer
resolves10.1038/nrd1470
Can the pharmaceutical industry reduce attrition rates?
resolves10.1016/S1359-6446(04)03163-0
From magic bullets to designed multiple ligands
resolves10.1016/j.sbi.2006.01.013
Can we rationally design promiscuous drugs?
resolves10.2174/138620711797537102
Virtual High Throughput Screening in New Lead Identification
resolves10.1080/17460441.2017.1280024
Computational polypharmacology: a new paradigm for drug discovery
resolves10.1016/j.drudis.2015.12.007
In silico methods to address polypharmacology: current status, applications and future perspectives
resolves10.2174/0929867311320130005
Predicting Targeted Polypharmacology for Drug Repositioning and Multi- Target Drug Discovery
resolves10.1080/17460441.2020.1767063
An up-to-date overview of computational polypharmacology in modern drug discovery
resolves10.1093/bib/bbz103
Computational/in silico methods in drug target and lead prediction
resolves10.1021/acs.jmedchem.8b00760
Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds
resolves10.1038/nature11159
Large-scale prediction and testing of drug activity on side-effect targets
resolves10.1021/jm200666a
Chemical Structural Novelty: On-Targets and Off-Targets
resolves10.1016/j.ddtec.2006.12.008
In silico target fishing: Predicting biological targets from chemical structure
resolves10.1016/j.compbiomed.2021.104851
In silico methods and tools for drug discovery
resolves10.1021/acs.jcim.7b00225
IFPTarget: A Customized Virtual Target Identification Method Based on Protein–Ligand Interaction Fingerprinting Analyses
resolves10.1093/nar/gkl114
TarFisDock: a web server for identifying drug targets with docking approach
resolves10.1093/nar/gkac860
MeDBA: the Metalloenzyme Data Bank and Analysis platform
resolves10.1016/j.ejmech.2021.113772
ProfKin: A comprehensive web server for structure-based kinase profiling
resolves10.3390/ijms20184331
Molecular Docking: Shifting Paradigms in Drug Discovery
resolves10.1038/nbt1273
Structure-based maximal affinity model predicts small-molecule druggability
resolves10.1016/j.jprot.2011.05.011
From in silico target prediction to multi-target drug design: Current databases, methods and applications
resolves10.1016/j.ymeth.2014.09.006
Tools for in silico target fishing
resolves10.1039/b409813g
Molecular similarity: a key technique in molecular informatics
resolves10.1021/ci9800211
Chemical Similarity Searching
resolves10.1021/ci034160g
Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR Modeling
resolves10.1021/jm020491t
Development and Validation of <i>k</i>-Nearest-Neighbor QSPR Models of Metabolic Stability of Drug Candidates
resolves10.1016/S0097-8485(01)00094-8
Drug design by machine learning: support vector machines for pharmaceutical data analysis
resolves10.1017/CBO9780511801389
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
resolves10.1021/ci060003g
Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases
resolves10.1121/1.400114
Classification of audiograms by sequential testing using a dynamic Bayesian procedure
resolves10.1016/j.neunet.2014.09.003
Deep learning in neural networks: An overview
resolves10.3389/fenvs.2016.00003
QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays
resolves10.1093/nar/gku293
SwissTargetPrediction: a web server for target prediction of bioactive small molecules
resolves10.1038/nbt1284
Relating protein pharmacology by ligand chemistry
resolves10.1021/acs.jcim.8b00524
Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning
resolves10.1093/nar/gkw1074
The ChEMBL database in 2017
resolves10.1007/s10822-016-9915-2
TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
resolves10.1093/bib/bbz026
Validation strategies for target prediction methods
resolves10.3389/fchem.2016.00015
How Reliable Are Ligand-Centric Methods for Target Fishing?
resolves10.1093/nar/gkl999
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
resolves10.1039/C9NP00064J
Natural allosteric modulators and their biological targets: molecular signatures and mechanisms
resolves10.1093/nar/gkz382
SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules
resolves10.1093/bib/bbz157
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
resolves10.1021/jm4004285
QSAR Modeling: Where Have You Been? Where Are You Going To?
resolves10.1021/ci8002914
Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients
resolves10.1371/journal.pone.0037608
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data
resolves10.1021/acs.jproteome.6b00618
Deep-Learning-Based Drug–Target Interaction Prediction
resolves10.3390/molecules23092208
Machine Learning for Drug-Target Interaction Prediction
resolves10.1038/s41573-019-0024-5
Applications of machine learning in drug discovery and development
resolves10.1093/bioinformatics/btt540
Shaping the interaction landscape of bioactive molecules
resolves10.1016/j.ejmech.2020.112644
A combinatorial target screening strategy for deorphaning macromolecular targets of natural product
resolves10.1038/nature08506
Predicting new molecular targets for known drugs
The 7 references without a DOI — listed, not checked
no DOI — not checkedA review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
no DOI — not checkedIn Silicotarget fishing: addressing a “big data” problem by ligand-based similarity rankings with data fusion
no DOI — not checkedConcepts and applications of molecular similarity
no DOI — not checkedMachine learning methods in chemoinformatics, Wiley interdisciplinary reviews: computational molecular
no DOI — not checkedSys Chem Biol
no DOI — not checkedThe polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data
no DOI — not checkedReliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation
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