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Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Nature of Statistical Learning Theory
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Graph kernels for chemical informatics
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Drug design by machine learning: support vector machines for pharmaceutical data analysis
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Active Learning with Support Vector Machines in the Drug Discovery Process
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Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier
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Virtual Screening of Molecular Databases Using a Support Vector Machine
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Virtual Screen for Ligands of Orphan G Protein-Coupled Receptors
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Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases
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Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors
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Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
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Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines
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Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors
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Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors
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A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1038/nature03196openalex-snapshot
Exploring biology with small organic molecules
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Global mapping of pharmacological space
inferredcites →https://doi.org/10.1038/nrg1317openalex-snapshot
Chemogenomics: an emerging strategy for rapid target and drug discovery
inferredcites →https://doi.org/10.1093/bioinformatics/btn409openalex-snapshot
Protein-ligand interaction prediction: an improved chemogenomics approach
inferredcites →https://doi.org/10.1111/j.1747-0285.2010.01059.xopenalex-snapshot
Potency‐Directed Similarity Searching Using Support Vector Machines
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A training algorithm for optimal margin classifiers
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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.
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Mapping of the Available Chemical Space versus the Chemical Universe of Lead‐Like Compounds
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Compound Structure‐Independent Activity Prediction in High‐Dimensional Target Space
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Enhancing Graph Neural Networks for Multi-target Activity Prediction Through Multi-task Learning and Knowledge Distillation
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Investigating Recurrent Neural Networks for Feature-Less Computational Drug Design
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Multi-task generative topographic mapping in virtual screening
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Virtual screening techniques in pharmaceutical research
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Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery
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Artificial intelligence systems for the design of magic shotgun drugs
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QSAR study of pyrazolo[1,5-a]pyrimidine derivative inhibitors of Chk1
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Drug/nondrug classification using Support Vector Machines with various feature selection strategies
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Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity
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Visualization and Interpretation of Support Vector Machine Activity Predictions
inferred← citeshttps://doi.org/10.1111/cbdd.12294openalex-snapshot
Modeling of Compound Profiling Experiments Using Support Vector Machines
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Spectrum of deep learning algorithms in drug discovery
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Inferring multi-target QSAR models with taxonomy-based multi-task learning
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A ranking method for the concurrent learning of compounds with various activity profiles
inferred← citeshttps://doi.org/10.12688/f1000research.3713.1openalex-snapshot
Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer
inferred← citeshttps://doi.org/10.1517/17460441.2014.866943openalex-snapshot
Support vector machines for drug discovery
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Use of machine learning approaches for novel drug discovery
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An Analysis of QSAR Research Based on Machine Learning Concepts
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Virtual Screening Meets Deep Learning
inferred← citeshttps://doi.org/10.2751/jcac.18.124openalex-snapshot
Small Random Forest Models for Effective Chemogenomic Active Learning
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Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape Similarity Profile Based on PDB Ligands as Molecular Descriptors
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Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes
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