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Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/3527603743.ch11openalex-snapshot
Structure Modification in Chemical Databases
inferredcites →https://doi.org/10.1016/j.bmc.2007.08.057openalex-snapshot
A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
inferredcites →https://doi.org/10.1016/j.ejmech.2008.03.004openalex-snapshot
Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1021/ci034107sopenalex-snapshot
Support Vector Machines for the Estimation of Aqueous Solubility
inferredcites →https://doi.org/10.1021/ci049820bopenalex-snapshot
QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine
inferredcites →https://doi.org/10.1021/ci050308fopenalex-snapshot
Development and Evaluation of an in Silico Model for hERG Binding
inferredcites →https://doi.org/10.1021/ci0503208openalex-snapshot
Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR
inferredcites →https://doi.org/10.1021/ci3001138openalex-snapshot
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs
inferredcites →https://doi.org/10.1021/ci300306aopenalex-snapshot
Prediction of Activity Cliffs Using Support Vector Machines
inferredcites →https://doi.org/10.1021/ci400001uopenalex-snapshot
Quantitative Structure–Activity Relationship Models of Clinical Pharmacokinetics: Clearance and Volume of Distribution
inferredcites →https://doi.org/10.1021/ci400165aopenalex-snapshot
Compound Optimization through Data Set-Dependent Chemical Transformations
inferredcites →https://doi.org/10.1021/ci500012nopenalex-snapshot
Quantitative Structure–Activity Relationship Models of Chemical Transformations from Matched Pairs Analyses
inferredcites →https://doi.org/10.1021/ci900450mopenalex-snapshot
Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
inferredcites →https://doi.org/10.1021/jm4004285openalex-snapshot
QSAR Modeling: Where Have You Been? Where Are You Going To?
inferredcites →https://doi.org/10.1021/jm401120gopenalex-snapshot
Recent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry
inferredcites →https://doi.org/10.1021/tx060230copenalex-snapshot
A Novel Approach Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) for Prediction of hERG Liability
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1111/j.1747-0285.2009.00814.xopenalex-snapshot
Quantitative Structure–Activity Relationship and Classification Analysis of Diaryl Ureas Against Vascular Endothelial Growth Factor Receptor‐2 Kinase Using Linear and Non‐Linear Models
inferredcites →https://doi.org/10.18637/jss.v011.i09openalex-snapshot
kernlab - An S4 Package for Kernel Methods in R
inferredcites →https://doi.org/10.5555/299094openalex-snapshot
Advances in kernel methods: support vector learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/minf.201600153openalex-snapshot
Predicting the Enzymatic Hydrolysis Half‐lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination
inferred← citeshttps://doi.org/10.1007/978-3-032-22193-3_34openalex-snapshot
GraphBioisostere: General Bioisostere Prediction Model with Deep Graph Neural Network
inferred← citeshttps://doi.org/10.1007/s10822-020-00315-zopenalex-snapshot
Advances in exploring activity cliffs
inferred← citeshttps://doi.org/10.1007/s10822-021-00380-yopenalex-snapshot
Prediction of activity cliffs on the basis of images using convolutional neural networks
inferred← citeshttps://doi.org/10.1007/s10822-022-00442-9openalex-snapshot
Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
inferred← citeshttps://doi.org/10.1007/s11227-026-08232-yopenalex-snapshot
GraphBioisostere: general bioisostere prediction model with deep graph neural network
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.12347-9openalex-snapshot
Matched Molecular Pair Analysis
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.05.006openalex-snapshot
Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00327openalex-snapshot
ACGCN: Graph Convolutional Networks for Activity Cliff Prediction between Matched Molecular Pairs
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01073openalex-snapshot
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00359openalex-snapshot
Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00298openalex-snapshot
Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00335openalex-snapshot
Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c01787openalex-snapshot
Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications
inferred← citeshttps://doi.org/10.1038/s41467-025-57582-3openalex-snapshot
A data-driven generative strategy to avoid reward hacking in multi-objective molecular design
inferred← citeshttps://doi.org/10.1039/9781782623731-00120openalex-snapshot
Chemical Similarity, Shape Matching and QSAR
inferred← citeshttps://doi.org/10.1111/cbdd.13663openalex-snapshot
Molecular design aided by random forests and synthesis of potent trypanocidal agents as cruzain inhibitors for Chagas disease treatment
inferred← citeshttps://doi.org/10.1177/0165551520920794openalex-snapshot
Topic extraction to provide an overview of research activities: The case of the high-temperature superconductor and simulation and modelling
inferred← citeshttps://doi.org/10.1186/s13321-023-00708-wopenalex-snapshot
Exploring QSAR models for activity-cliff prediction
inferred← citeshttps://doi.org/10.2751/jcac.16.15openalex-snapshot
A Mini-review on Chemoinformatics Approaches for Drug Discovery
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New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
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