333 related articles for article (PubMed ID: 22424085)
1. Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.
Sato T; Yuki H; Takaya D; Sasaki S; Tanaka A; Honma T
J Chem Inf Model; 2012 Apr; 52(4):1015-26. PubMed ID: 22424085
[TBL] [Abstract][Full Text] [Related]
2. Beware of machine learning-based scoring functions-on the danger of developing black boxes.
Gabel J; Desaphy J; Rognan D
J Chem Inf Model; 2014 Oct; 54(10):2807-15. PubMed ID: 25207678
[TBL] [Abstract][Full Text] [Related]
3. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins.
Sugaya N
J Chem Inf Model; 2014 Oct; 54(10):2751-63. PubMed ID: 25220713
[TBL] [Abstract][Full Text] [Related]
4. Structure-based virtual screening for novel ligands.
Pitt WR; Calmiano MD; Kroeplien B; Taylor RD; Turner JP; King MA
Methods Mol Biol; 2013; 1008():501-19. PubMed ID: 23729265
[TBL] [Abstract][Full Text] [Related]
5. Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.
Meslamani J; Li J; Sutter J; Stevens A; Bertrand HO; Rognan D
J Chem Inf Model; 2012 Apr; 52(4):943-55. PubMed ID: 22480372
[TBL] [Abstract][Full Text] [Related]
6. Molecular fields in ligand discovery.
Gane PJ; Chan AW
Methods Mol Biol; 2013; 1008():479-99. PubMed ID: 23729264
[TBL] [Abstract][Full Text] [Related]
7. Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach.
Sugaya N
J Chem Inf Model; 2013 Oct; 53(10):2525-37. PubMed ID: 24020509
[TBL] [Abstract][Full Text] [Related]
8. SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition.
Wei NN; Hamza A
J Chem Inf Model; 2014 Jan; 54(1):338-46. PubMed ID: 24328054
[TBL] [Abstract][Full Text] [Related]
9. Ligand aligning method for molecular docking: alignment of property-weighted vectors.
Joung JY; Nam KY; Cho KH; No KT
J Chem Inf Model; 2012 Apr; 52(4):984-95. PubMed ID: 22471323
[TBL] [Abstract][Full Text] [Related]
10. Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design.
Du QS; Gao J; Wei YT; Du LQ; Wang SQ; Huang RB
J Chem Inf Model; 2012 Apr; 52(4):996-1004. PubMed ID: 22480344
[TBL] [Abstract][Full Text] [Related]
11. Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments.
Karaboga AS; Petronin F; Marchetti G; Souchet M; Maigret B
J Mol Graph Model; 2013 Apr; 41():20-30. PubMed ID: 23467019
[TBL] [Abstract][Full Text] [Related]
12. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.
Fang J; Yang R; Gao L; Zhou D; Yang S; Liu AL; Du GH
J Chem Inf Model; 2013 Nov; 53(11):3009-20. PubMed ID: 24144102
[TBL] [Abstract][Full Text] [Related]
13. Unconventional 2D shape similarity method affords comparable enrichment as a 3D shape method in virtual screening experiments.
Ebalunode JO; Zheng W
J Chem Inf Model; 2009 Jun; 49(6):1313-20. PubMed ID: 19480404
[TBL] [Abstract][Full Text] [Related]
14. SDOVS: a solvent dipole ordering-based method for virtual screening.
Murata K; Nagata N; Nakanishi I; Kitaura K
J Comput Chem; 2010 Nov; 31(15):2714-22. PubMed ID: 20839298
[TBL] [Abstract][Full Text] [Related]
15. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors.
Geppert H; Humrich J; Stumpfe D; Gärtner T; Bajorath J
J Chem Inf Model; 2009 Apr; 49(4):767-79. PubMed ID: 19309114
[TBL] [Abstract][Full Text] [Related]
16. Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape Similarity Profile Based on PDB Ligands as Molecular Descriptors.
Hu B; Kuang ZK; Feng SY; Wang D; He SB; Kong DX
Molecules; 2016 Nov; 21(11):. PubMed ID: 27869685
[TBL] [Abstract][Full Text] [Related]
17. Pharmacophore based virtual screening, molecular docking studies to design potent heat shock protein 90 inhibitors.
Sakkiah S; Thangapandian S; John S; Lee KW
Eur J Med Chem; 2011 Jul; 46(7):2937-47. PubMed ID: 21531051
[TBL] [Abstract][Full Text] [Related]
18. Computation of binding energies including their enthalpy and entropy components for protein-ligand complexes using support vector machines.
Koppisetty CA; Frank M; Kemp GJ; Nyholm PG
J Chem Inf Model; 2013 Oct; 53(10):2559-70. PubMed ID: 24050538
[TBL] [Abstract][Full Text] [Related]
19. Protein-chemical interaction prediction via kernelized sparse learning SVM.
Shi Y; Zhang X; Liao X; Lin G; Schuurmans D
Pac Symp Biocomput; 2013; ():41-52. PubMed ID: 23424110
[TBL] [Abstract][Full Text] [Related]
20. New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands.
Kurczab R; Bojarski AJ
J Chem Inf Model; 2013 Dec; 53(12):3233-43. PubMed ID: 24245803
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]