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Ligand Efficiency-Based Support Vector Regression Models for Predicting Bioactivities of Ligands to Drug Target Proteins

https://doi.org/10.1021/ci5003262
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What this work says

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

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An Introduction to Categorical Data Analysis
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Virtual and experimental high-throughput screening (HTS) in search of novel inosine 5′-monophosphate dehydrogenase II (IMPDH II) inhibitors
inferredcites →https://doi.org/10.1016/j.aca.2012.09.021openalex-snapshot
Large-scale prediction of drug–target interactions using protein sequences and drug topological structures
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Kernel-based data fusion improves the drug–protein interaction prediction
inferredcites →https://doi.org/10.1016/j.drudis.2010.08.004openalex-snapshot
Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation
inferredcites →https://doi.org/10.1016/j.ejmech.2014.02.034openalex-snapshot
Virtual fragment screening on GPCRs: A case study on dopamine D3 and histamine H4 receptors
inferredcites →https://doi.org/10.1016/s1359-6446(05)03386-6openalex-snapshot
Ligand efficiency indices as guideposts for drug discovery
inferredcites →https://doi.org/10.1021/ci00053a005openalex-snapshot
Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions
inferredcites →https://doi.org/10.1021/ci050006dopenalex-snapshot
Virtual Screen for Ligands of Orphan G Protein-Coupled Receptors
inferredcites →https://doi.org/10.1021/ci200264hopenalex-snapshot
Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation
inferredcites →https://doi.org/10.1021/ci400240uopenalex-snapshot
Training Based on Ligand Efficiency Improves Prediction of Bioactivities of Ligands and Drug Target Proteins in a Machine Learning Approach
inferredcites →https://doi.org/10.1021/ci800447gopenalex-snapshot
Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands
inferredcites →https://doi.org/10.1021/ci900004aopenalex-snapshot
Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors
inferredcites →https://doi.org/10.1021/ci9002624openalex-snapshot
Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects
inferredcites →https://doi.org/10.1021/jm100118xopenalex-snapshot
An Analysis of the Binding Efficiencies of Drugs and Their Leads in Successful Drug Discovery Programs
inferredcites →https://doi.org/10.1021/jm101273eopenalex-snapshot
A Practical Use of Ligand Efficiency Indices Out of the Fragment-Based Approach: Ligand Efficiency-Guided Lead Identification of Soluble Epoxide Hydrolase Inhibitors
inferredcites →https://doi.org/10.1021/jm901647popenalex-snapshot
Structure-Based Discovery of Novel Chemotypes for Adenosine A2AReceptor Antagonists
inferredcites →https://doi.org/10.1038/msb.2011.5openalex-snapshot
Analysis of multiple compound–protein interactions reveals novel bioactive molecules
inferredcites →https://doi.org/10.1038/nrd2445openalex-snapshot
The influence of drug-like concepts on decision-making in medicinal chemistry
inferredcites →https://doi.org/10.1038/nrd4163openalex-snapshot
The role of ligand efficiency metrics in drug discovery
inferredcites →https://doi.org/10.1093/bioinformatics/btm580openalex-snapshot
Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor
inferredcites →https://doi.org/10.1093/bioinformatics/btn409openalex-snapshot
Protein-ligand interaction prediction: an improved chemogenomics approach
inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
inferredcites →https://doi.org/10.1093/nar/gkm948openalex-snapshot
GLIDA: GPCR ligand database for chemical genomics drug discovery database and tools update
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/gkr988openalex-snapshot
KEGG for integration and interpretation of large-scale molecular data sets
inferredcites →https://doi.org/10.1186/1471-2105-9-363openalex-snapshot
Virtual screening of GPCRs: An in silico chemogenomics approach
inferredcites →https://doi.org/10.1186/1758-2946-3-22openalex-snapshot
Predicting a small molecule-kinase interaction map: A machine learning approach
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A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

What points at this work

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

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Predicting the Enzymatic Hydrolysis Half‐lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination
inferred← citeshttps://doi.org/10.1007/978-3-319-46307-0_30openalex-snapshot
Ligand Affinity Prediction with Multi-pattern Kernels
inferred← citeshttps://doi.org/10.1007/978-3-319-71246-8_21openalex-snapshot
Co-Regularised Support Vector Regression
inferred← citeshttps://doi.org/10.1007/s11030-021-10217-3openalex-snapshot
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
inferred← citeshttps://doi.org/10.1016/j.bmc.2020.115794openalex-snapshot
Optimization of kinetic stabilizers of tetrameric transthyretin: A prospective ligand efficiency-guided approach
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00136openalex-snapshot
Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00431openalex-snapshot
Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities
inferred← citeshttps://doi.org/10.1080/14498596.2016.1215272openalex-snapshot
Tide modelling using support vector machine regression
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Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective
inferred← citeshttps://doi.org/10.1109/cec.2015.7257138openalex-snapshot
Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm
inferred← citeshttps://doi.org/10.1109/icdmw.2016.0044openalex-snapshot
Ligand-Based Virtual Screening with Co-regularised Support Vector Regression
inferred← citeshttps://doi.org/10.1111/cbdd.12619openalex-snapshot
Ligand Efficiency Outperforms pIC 50 on Both 2D MLR and 3D Co MFA Models: A Case Study on AR Antagonists
inferred← citeshttps://doi.org/10.1111/cbdd.13150openalex-snapshot
Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity
inferred← citeshttps://doi.org/10.1155/2015/916240openalex-snapshot
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
inferred← citeshttps://doi.org/10.1186/s13321-015-0110-6openalex-snapshot
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter
inferred← citeshttps://doi.org/10.1186/s13321-018-0310-yopenalex-snapshot
Implicit-descriptor ligand-based virtual screening by means of collaborative filtering
inferred← citeshttps://doi.org/10.1517/17460441.2015.1083006openalex-snapshot
Quantitative structure–activity relationship: promising advances in drug discovery platforms
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Revolutionizing Drug Development: Harnessing Artificial Intelligence in Pharmaceutical Sciences
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