236 related articles for article (PubMed ID: 24490838)
1. Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models.
Toplak M; Močnik R; Polajnar M; Bosnić Z; Carlsson L; Hasselgren C; Demšar J; Boyer S; Zupan B; Stålring J
J Chem Inf Model; 2014 Feb; 54(2):431-41. PubMed ID: 24490838
[TBL] [Abstract][Full Text] [Related]
2. Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients.
Stålring J; Almeida PR; Carlsson L; Helgee Ahlberg E; Hasselgren C; Boyer S
J Chem Inf Model; 2013 Aug; 53(8):2001-17. PubMed ID: 23845139
[TBL] [Abstract][Full Text] [Related]
3. Merging applicability domains for in silico assessment of chemical mutagenicity.
Liu R; Wallqvist A
J Chem Inf Model; 2014 Mar; 54(3):793-800. PubMed ID: 24494696
[TBL] [Abstract][Full Text] [Related]
4. Prediction of antibacterial compounds by machine learning approaches.
Yang XG; Chen D; Wang M; Xue Y; Chen YZ
J Comput Chem; 2009 Jun; 30(8):1202-11. PubMed ID: 18988254
[TBL] [Abstract][Full Text] [Related]
5. Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds.
Liu R; Wallqvist A
J Chem Inf Model; 2019 Jan; 59(1):181-189. PubMed ID: 30404432
[TBL] [Abstract][Full Text] [Related]
6. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.
Zhu H; Tropsha A; Fourches D; Varnek A; Papa E; Gramatica P; Oberg T; Dao P; Cherkasov A; Tetko IV
J Chem Inf Model; 2008 Apr; 48(4):766-84. PubMed ID: 18311912
[TBL] [Abstract][Full Text] [Related]
7. General Approach to Estimate Error Bars for Quantitative Structure-Activity Relationship Predictions of Molecular Activity.
Liu R; Glover KP; Feasel MG; Wallqvist A
J Chem Inf Model; 2018 Aug; 58(8):1561-1575. PubMed ID: 29949366
[TBL] [Abstract][Full Text] [Related]
8. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection.
Tetko IV; Sushko I; Pandey AK; Zhu H; Tropsha A; Papa E; Oberg T; Todeschini R; Fourches D; Varnek A
J Chem Inf Model; 2008 Sep; 48(9):1733-46. PubMed ID: 18729318
[TBL] [Abstract][Full Text] [Related]
9. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
Schroeter TS; Schwaighofer A; Mika S; Ter Laak A; Suelzle D; Ganzer U; Heinrich N; Müller KR
J Comput Aided Mol Des; 2007 Sep; 21(9):485-98. PubMed ID: 17632688
[TBL] [Abstract][Full Text] [Related]
10. Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions.
Myint KZ; Ma C; Wang L; Xie XQ
SAR QSAR Environ Res; 2011 Jun; 22(3):385-410. PubMed ID: 21598200
[TBL] [Abstract][Full Text] [Related]
11. The importance of outlier detection and training set selection for reliable environmental QSAR predictions.
Furusjö E; Svenson A; Rahmberg M; Andersson M
Chemosphere; 2006 Mar; 63(1):99-108. PubMed ID: 16153688
[TBL] [Abstract][Full Text] [Related]
12. Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.
Chen H; Carlsson L; Eriksson M; Varkonyi P; Norinder U; Nilsson I
J Chem Inf Model; 2013 Jun; 53(6):1324-36. PubMed ID: 23789733
[TBL] [Abstract][Full Text] [Related]
13. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.
Liu R; Schyman P; Wallqvist A
J Chem Inf Model; 2015 Aug; 55(8):1566-75. PubMed ID: 26170251
[TBL] [Abstract][Full Text] [Related]
14. Assessing the reliability of a QSAR model's predictions.
He L; Jurs PC
J Mol Graph Model; 2005 Jun; 23(6):503-23. PubMed ID: 15896992
[TBL] [Abstract][Full Text] [Related]
15. Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome.
Martin E; Mukherjee P
J Chem Inf Model; 2012 Jan; 52(1):156-70. PubMed ID: 22133092
[TBL] [Abstract][Full Text] [Related]
16. Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.
Shen M; Béguin C; Golbraikh A; Stables JP; Kohn H; Tropsha A
J Med Chem; 2004 Apr; 47(9):2356-64. PubMed ID: 15084134
[TBL] [Abstract][Full Text] [Related]
17. Intelligently Applying Artificial Intelligence in Chemoinformatics.
Sharma S; Sharma D
Curr Top Med Chem; 2018; 18(20):1804-1826. PubMed ID: 30465503
[TBL] [Abstract][Full Text] [Related]
18. Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.
Mohr JA; Jain BJ; Obermayer K
J Chem Inf Model; 2008 Sep; 48(9):1868-81. PubMed ID: 18767832
[TBL] [Abstract][Full Text] [Related]
19. Ranking chemical structures for drug discovery: a new machine learning approach.
Agarwal S; Dugar D; Sengupta S
J Chem Inf Model; 2010 May; 50(5):716-31. PubMed ID: 20387860
[TBL] [Abstract][Full Text] [Related]
20. Fuzzy ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular data set.
Andonie R; Fabry-Asztalos L; Abdul-Wahid CB; Abdul-Wahid S; Barker GI; Magill LC
IEEE/ACM Trans Comput Biol Bioinform; 2011; 8(1):80-93. PubMed ID: 21071799
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]