153 related articles for article (PubMed ID: 30497262)
1. In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints.
Koutsoukas A; Chang G; Keefer CE
J Chem Inf Model; 2019 Jan; 59(1):477-485. PubMed ID: 30497262
[TBL] [Abstract][Full Text] [Related]
2. QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.
Fu L; Yang ZY; Yang ZJ; Yin MZ; Lu AP; Chen X; Liu S; Hou TJ; Cao DS
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33418563
[TBL] [Abstract][Full Text] [Related]
3. Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses.
Beck JM; Springer C
J Chem Inf Model; 2014 Apr; 54(4):1226-34. PubMed ID: 24605924
[TBL] [Abstract][Full Text] [Related]
4. Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion.
Yang ZY; Fu L; Lu AP; Liu S; Hou TJ; Cao DS
J Cheminform; 2021 Nov; 13(1):86. PubMed ID: 34774096
[TBL] [Abstract][Full Text] [Related]
5. The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization.
Lumley JA; Desai P; Wang J; Cahya S; Zhang H
J Chem Inf Model; 2020 Oct; 60(10):4757-4771. PubMed ID: 32975944
[TBL] [Abstract][Full Text] [Related]
6. Building a Quantitative Structure-Property Relationship (QSPR) Model.
Clark RD; Daga PR
Methods Mol Biol; 2019; 1939():139-159. PubMed ID: 30848460
[TBL] [Abstract][Full Text] [Related]
7. Prospective Prediction of Antitarget Activity by Matched Molecular Pairs Analysis.
Warner DJ; Bridgland-Taylor MH; Sefton CE; Wood DJ
Mol Inform; 2012 May; 31(5):365-8. PubMed ID: 27477265
[TBL] [Abstract][Full Text] [Related]
8. Confident application of a global human liver microsomal activity QSAR.
Stålring J; Sohlenius-Sternbeck AK; Terelius Y; Parkes K
Future Med Chem; 2018 Jul; 10(13):1575-1588. PubMed ID: 29953260
[TBL] [Abstract][Full Text] [Related]
9. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.
Tropsha A; Golbraikh A
Curr Pharm Des; 2007; 13(34):3494-504. PubMed ID: 18220786
[TBL] [Abstract][Full Text] [Related]
10. Computer-Aided Design of Orally Bioavailable Pyrrolidine Carboxamide Inhibitors of Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Pharmacokinetic Profiles.
Kouassi AF; Kone M; Keita M; Esmel A; Megnassan E; N'Guessan YT; Frecer V; Miertus S
Int J Mol Sci; 2015 Dec; 16(12):29744-71. PubMed ID: 26703572
[TBL] [Abstract][Full Text] [Related]
11. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.
Martin E; Mukherjee P; Sullivan D; Jansen J
J Chem Inf Model; 2011 Aug; 51(8):1942-56. PubMed ID: 21667971
[TBL] [Abstract][Full Text] [Related]
12. QSAR classification model for antibacterial compounds and its use in virtual screening.
Singh N; Chaudhury S; Liu R; AbdulHameed MD; Tawa G; Wallqvist A
J Chem Inf Model; 2012 Oct; 52(10):2559-69. PubMed ID: 23013546
[TBL] [Abstract][Full Text] [Related]
13. Extraction of tacit knowledge from large ADME data sets via pairwise analysis.
Keefer CE; Chang G; Kauffman GW
Bioorg Med Chem; 2011 Jun; 19(12):3739-49. PubMed ID: 21616672
[TBL] [Abstract][Full Text] [Related]
14. In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.
Chaudhari P; Bari S
Mol Divers; 2016 Feb; 20(1):41-53. PubMed ID: 26416560
[TBL] [Abstract][Full Text] [Related]
15. Statistical molecular design of balanced compound libraries for QSAR modeling.
Linusson A; Elofsson M; Andersson IE; Dahlgren MK
Curr Med Chem; 2010; 17(19):2001-16. PubMed ID: 20423313
[TBL] [Abstract][Full Text] [Related]
16. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.
Zhang S; Golbraikh A; Oloff S; Kohn H; Tropsha A
J Chem Inf Model; 2006; 46(5):1984-95. PubMed ID: 16995729
[TBL] [Abstract][Full Text] [Related]
17. Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents.
Zhang S; Wei L; Bastow K; Zheng W; Brossi A; Lee KH; Tropsha A
J Comput Aided Mol Des; 2007; 21(1-3):97-112. PubMed ID: 17340042
[TBL] [Abstract][Full Text] [Related]
18. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.
Roy K; Mitra I
Comb Chem High Throughput Screen; 2011 Jul; 14(6):450-74. PubMed ID: 21521150
[TBL] [Abstract][Full Text] [Related]
19. Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process.
Sushko Y; Novotarskyi S; Körner R; Vogt J; Abdelaziz A; Tetko IV
J Cheminform; 2014; 6(1):48. PubMed ID: 25544551
[TBL] [Abstract][Full Text] [Related]
20. Energy-Based Pharmacophore and Three-Dimensional Quantitative Structure--Activity Relationship (3D-QSAR) Modeling Combined with Virtual Screening To Identify Novel Small-Molecule Inhibitors of Silent Mating-Type Information Regulation 2 Homologue 1 (SIRT1).
Pulla VK; Sriram DS; Viswanadha S; Sriram D; Yogeeswari P
J Chem Inf Model; 2016 Jan; 56(1):173-87. PubMed ID: 26636371
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]