ArticleACS omega2017
Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction.
Article in ACS omega, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed.
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- Rapid Assessment of Virtually Synthesizable Chemical Structures via Support Vector Machine Models.Molecular informatics · 2025Article
- Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.Briefings in bioinformatics · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.The Journal of international medical research · 2024Article
- Multivariate and regional age-related change in basal ganglia iron in neonates.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation.Journal of cheminformatics · 2023Article
- Machine Learning to Predict the Adsorption Capacity of Microplastics.Nanomaterials (Basel, Switzerland) · 2023Article
- Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network.Sensors (Basel, Switzerland) · 2023Article
- Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data.Neural computing & applications · 2023Article
- Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.Molecules (Basel, Switzerland) · 2022Article
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- Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In addition, support vector regression (SVR) has become a preferred approach for modeling nonlinear structure-activity relationships and predicting compound potency values. For the closely related SVM and SVR methods, fingerprints (i.e., bit string or feature set representations of chemical structure and properties) are generally preferred descriptors. Herein, we have compared SVM and SVR calculations for the same compound data sets to evaluate which features are responsible for predictions. On the basis of systematic feature weight analysis, rather surprising results were obtained. Fingerprint features were frequently identified that contributed differently to the corresponding SVM and SVR models. The overlap between feature sets determining the predictive performance of SVM and SVR was only very small. Furthermore, features were identified that had opposite effects on SVM and SVR predictions. Feature weight analysis in combination with feature mapping made it also possible to interpret individual predictions, thus balancing the black box character of SVM/SVR modeling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.