Chemistry Reference
In-Depth Information
[726] Carosati E, Lemoine H, Spogli R, Grittner D, Mannhold R, Tabarrini O, Sabatini S,
Cecche] tti V. Binding studies and GRIND/ALMOND-based 3D QSAR analysis of
benzothiazine type K(ATP)-channel openers. Bioorg. Med. Chem. 2005, 13:5581-5591.
[727] Benedetti P, Mannhold R, Cruciani G, Ottaviani G. GRIND/ALMOND investigations on
CysLT(1) receptor antagonists of the quinolinyl(bridged)aryl type. Bioorg. Med. Chem.
2004, 12: 3607-3617.
[728] Sciabola S, Carosati E, Baroni M, Mannhold R. Comparison of Ligand-Based and
Structure-Based 3D-QSAR:A Case Study on (Aryl-)Bridged 2-Aminobenzonitriles
Inhibiting HIV-1 Reverse Transcriptase. J. Med. Chem. 2005, 48: 3756-3767.
[729] Cianchetta G, Singleton RW, Zhang M, Wildgoose M, Giesing D, Fravolini A, Cruciani G,
Vaz RJ. A pharmacophore hypothesis for P-glycoprotein substrate recognition using
GRIND-based 3D-QSAR. J. Med. Chem. 2005, 48: 2927-2935.
[730] Fontaine F, Pastor M, Zamora I, Sanz F. Anchor-GRIND: Filling the gap between standard
3D-QSAR and the GRid-INdependent Descriptors. J. Med. Chem. 2005, 48: 2687-2694.
[731] Li Q, Jørgensen FS, Oprea T, Brunak S, Taboureau O. hERG classification model based on
a combination of support vector machine method and GRIND descriptors. Mol. Pharm.
2008, 5: 117-127.
[732] Vyas V, Jain A, Jain A, Gupta A. Virtual Screening: A Fast Tool for Drug Design. Sci
Pharm. 2008, 76: 333-360.
[733] Ahlstrm MM, Ridderstrm M, Luthman K, Zamora I. Virtual Screening and Scaffold
Hopping Based on GRID Molecular Interaction Fields.
J. Chem. Inf. Model. 2005, 45:
1313-1323.
[734] Schneider G, Böhm H. Virtual screening and fast automated docking methods. Drug
Discov. Today. 2002,7:64-70.
[735] Neves MAC, Dinis TCP, Colombo G, Sa ML, Fast M. Fast Three Dimensional
Pharmacophore Virtual Screening of New Potent Non-Steroid Aromatase Inhibitors J. Med.
Chem. 2009, 52: 143-150.
[736] Wolohan PRN, Clark RD. Predicting drug pharmacokinetic properties using molecular
interaction fields and SIMCA. J. Computer-Aided Mol. Design 2003,17: 65-76.
[737] MODI S. Computational approaches to the understanding of ADMET properties and
problems. Drug Discov. Today. 2003, 8: 621-623.
[738]] O'Brien SE, Groot MJ. Greater than the sum of its parts: combining models for useful
ADMET prediction. J. Med. Chem. 2005, 48: 1287-1291.
[739] Cruciani G., Pastor M, Guba W. VolSurf: a new tool for the pharmacokonetic optimization
of lead compounds. Eur. J. Pharm. Sci. 2000, 2: S29-S39.
[740] Cruciani G, Crivori P, Carrupt PA, Testa B. Cruciani G, Crivori P, Carrupt PA, Testa B. J.
Mol. Struct., 503, 17. 2000. J. Mol. Struct. 2000, 503: 17-30.
[741] Doddareddy MR, Cho YS, Koh HY, Kim DH, Pae AN.
In silico
renal clearance model
using classical Volsurf approach. J. Chem. Inf. Model. 2006, 46: 1312-1320.
[742] Van de Waterbeemd H, Lennernäs H, Artursson P. Drug Bioavailability: Estimation of
Solubility, Permeability, Absorption and Bioavailability, 2003; WILEY-VCH, Weinheim.
[743] Milletti F, Storchi L, Sforna G, Cruciani G, New and Original p
K
a
Prediction Method
Using Grid Molecular Interaction Fields. J. Chem. Inf. Model. 2007, 47: 2172-2181.
[744] Sciabola S, Stanton RV, Mills JE, Floco MM, Baroni M, Cruciani G, Perrucio F and Mason
JS. High-throughput virtual screening of proteins using GRID molecular interaction fields.
Bioinformatics 2009, 25:3185-3200.
