These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
209 related articles for article (PubMed ID: 30023518)
1. Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction. Rodríguez-Pérez R; Vogt M; Bajorath J ACS Omega; 2017 Oct; 2(10):6371-6379. PubMed ID: 30023518 [TBL] [Abstract][Full Text] [Related]
2. Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling. Miyao T; Funatsu K; Bajorath J J Chem Inf Model; 2019 Mar; 59(3):983-992. PubMed ID: 30547580 [TBL] [Abstract][Full Text] [Related]
3. Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis. Balfer J; Bajorath J PLoS One; 2015; 10(3):e0119301. PubMed ID: 25742011 [TBL] [Abstract][Full Text] [Related]
4. Determination of Meta-Parameters for Support Vector Machine Linear Combinations. Jasial S; Balfer J; Vogt M; Bajorath J Mol Inform; 2015 Feb; 34(2-3):127-33. PubMed ID: 27490035 [TBL] [Abstract][Full Text] [Related]
5. Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. Rodríguez-Pérez R; Bajorath J J Comput Aided Mol Des; 2022 May; 36(5):355-362. PubMed ID: 35304657 [TBL] [Abstract][Full Text] [Related]
6. Non-linear modeling and chemical interpretation with aid of support vector machine and regression. Hasegawa K; Funatsu K Curr Comput Aided Drug Des; 2010 Mar; 6(1):24-36. PubMed ID: 20370693 [TBL] [Abstract][Full Text] [Related]
7. Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. Rodríguez-Pérez R; Vogt M; Bajorath J J Chem Inf Model; 2017 Apr; 57(4):710-716. PubMed ID: 28376613 [TBL] [Abstract][Full Text] [Related]
8. Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions. Su BH; Tu YS; Esposito EX; Tseng YJ J Chem Inf Model; 2012 Jun; 52(6):1660-73. PubMed ID: 22642982 [TBL] [Abstract][Full Text] [Related]
9. Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure-Potency Fingerprint. Janela T; Takeuchi K; Bajorath J Biomolecules; 2023 Feb; 13(2):. PubMed ID: 36830761 [TBL] [Abstract][Full Text] [Related]
10. Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression. Horvath D; Marcou G; Varnek A; Kayastha S; de la Vega de León A; Bajorath J J Chem Inf Model; 2016 Sep; 56(9):1631-40. PubMed ID: 27564682 [TBL] [Abstract][Full Text] [Related]
11. Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints. Yuan Y; Zheng F; Zhan CG AAPS J; 2018 Mar; 20(3):54. PubMed ID: 29564576 [TBL] [Abstract][Full Text] [Related]
12. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. Wassermann AM; Geppert H; Bajorath J J Chem Inf Model; 2009 Mar; 49(3):582-92. PubMed ID: 19249858 [TBL] [Abstract][Full Text] [Related]
13. Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships. Miyao T; Jasial S; Bajorath J; Funatsu K J Comput Aided Mol Des; 2019 Aug; 33(8):729-743. PubMed ID: 31435894 [TBL] [Abstract][Full Text] [Related]
14. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition. Melvin I; Ie E; Kuang R; Weston J; Stafford WN; Leslie C BMC Bioinformatics; 2007 May; 8 Suppl 4(Suppl 4):S2. PubMed ID: 17570145 [TBL] [Abstract][Full Text] [Related]
15. Prediction of compound potency changes in matched molecular pairs using support vector regression. de la Vega de León A; Bajorath J J Chem Inf Model; 2014 Oct; 54(10):2654-63. PubMed ID: 25191787 [TBL] [Abstract][Full Text] [Related]
16. [A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model]. Zhang J; Zhang D; Zhang W; Xie H Se Pu; 2012 Sep; 30(9):857-63. PubMed ID: 23285964 [TBL] [Abstract][Full Text] [Related]
17. Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors. Xia Z; Yan A Mol Divers; 2017 Aug; 21(3):661-675. PubMed ID: 28484935 [TBL] [Abstract][Full Text] [Related]
18. Cancer survival classification using integrated data sets and intermediate information. Kim S; Park T; Kon M Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860 [TBL] [Abstract][Full Text] [Related]
19. Bit silencing in fingerprints enables the derivation of compound class-directed similarity metrics. Wang Y; Bajorath J J Chem Inf Model; 2008 Sep; 48(9):1754-9. PubMed ID: 18698839 [TBL] [Abstract][Full Text] [Related]
20. Seminal quality prediction using data mining methods. Sahoo AJ; Kumar Y Technol Health Care; 2014; 22(4):531-45. PubMed ID: 24898862 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]