version date: 1 December 2006 Table irod Molecule Ghose/Crippen Villar Limonene s 3.01 3.75 Limonene r 3.01 3.76 Trans-3-Methylcyclohexanecarboxylic acid S, S 2 1.10 Trans-3-Methylcyclohexanecarboxylic acid Rr 2 1.12 Penicillamine s Penicillamine r 0.38 0.30 Carvone s 2.41 2.33 Carvone Homocamfin s 2.84 2.33 Homocamfin r 2.84 2.34 N-acetyl-penicillamine S 0.45 0.30 N-acetyl-penicillamine R 045 0.27 profen s 3.72 3.75 Ibuprofen R(2) 3.66 (Using the Ghose-Crippen and the Villar methods in different environments or their implementation in different SPARTAN versions may lead to different absolute values but the trend should be the same(see text) (Every time you use crystallographic files carefully check their correctness(see the papers by Cozzini et al. and Spy- rakis et al. ) An ibuprofen entry with a wrong chirality is deposited in the CSD. *CSD needs to be licensed from the Cambridge Crystallographic Data Centre or one of its distributors As you can see, the Villar method is quite discriminating of the two enantiomers of the same mole- cule, while the Ghose-Crippen method gives the same value in both cases 1)Compare and comment on the results, observing how hydrophobicity changes according to the different structure and substituent chemical nature. Check for possible changes due to the different semi-empirical(AMI or PM3)used )Search the Internet for databases(see references) from which you can download experimental log P values. Whether it is possible(the available files are in a"chemical format )to cover the da- taset to a format suitable to be read in spartan orde novo,. sketch the structure in a suitable format for SPARTAN. If necessary, you can download the software BABEL (http://www.eyesopen.com/products/applications/babel.html).BesureallthelogPvaluesyouuse to build your dataset are homogeneous, which is determined by the same or quite close methods Homogeneity also applies to biological activity tests, which is one of the prerequisites to make con- sistent not"illusive""QSARs). Split your dataset, selecting rigid structures for calculations by the Ghose-Crippen method and conformationally flexible structures for the ones by the villar method Calculate the log Ps using both the Ghose-Crippen and the Villar methods. Prepare a diagram of experimental vS calculated values(Microsoft Excel can be used) and find for the best method to calculate your split dataset. Validate your "model", mixing rigid and flexible structures and calcu- late again using both the methods. Log Ps for flexible structures should be worst calculated by the Ghose-Crippen method. Extend and compare the analysis by the HiNT software(see the paper <www.iupac.org/publications/cd/medicinal_chemistry/>10 Table II(1) Molecule Ghose/Crippen Villar Limonene S 3.01 3.75 Limonene R 3.01 3.76 Trans-3-Methylcyclohexanecarboxylic acid S,S 2 1.10 Trans-3-Methylcyclohexanecarboxylic acid R,R 2 1.12 Penicillamine S –0.38 –0.50 Penicillamine R –0.38 –0.30 Carvone S 2.41 2.33 Carvone R 2.41 2.38 Homocamfin S 2.84 2.33 Homocamfin R 2.84 2.34 N-acetyl-penicillamine S –0.45 0.30 N-acetyl-penicillamine R –0.45 0.27 Ibuprofen S(2) 3.72 3.75 Ibuprofen R (2) 3.66 3.75 (1)Using the Ghose–Crippen and the Villar methods in different environments or their implementation in different SPARTAN versions may lead to different absolute values but the trend should be the same (see text). (2)Every time you use crystallographic files carefully check their correctness (see the papers by Cozzini et al. and Spy￾rakis et al.). An ibuprofen entry with a wrong chirality is deposited in the CSD. *CSD needs to be licensed from the Cambridge Crystallographic Data Centre or one of its distributors. As you can see, the Villar method is quite discriminating of the two enantiomers of the same mole￾cule, while the Ghose–Crippen method gives the same value in both cases. 1) Compare and comment on the results, observing how hydrophobicity changes according to the different structure and substituent chemical nature. Check for possible changes due to the different “semi-empirical” (AM1 or PM3) used. 2) Search the Internet for databases (see references) from which you can download experimental log P values. Whether it is possible (the available files are in a “chemical format”) to cover the da￾taset to a format suitable to be read in SPARTAN or “de novo”, sketch the structure in a suitable format for SPARTAN. If necessary, you can download the software BABEL (http://www.eyesopen.com/products/applications/babel.html). Be sure all the log P values you use to build your dataset are homogeneous, which is determined by the same or quite close methods (homogeneity also applies to biological activity tests, which is one of the prerequisites to make con￾sistent not “illusive” QSARs). Split your dataset, selecting rigid structures for calculations by the Ghose–Crippen method and conformationally flexible structures for the ones by the Villar method. Calculate the log Ps using both the Ghose–Crippen and the Villar methods. Prepare a diagram of experimental vs. calculated values (Microsoft Excel can be used) and find for the best method to calculate your split dataset. Validate your “model”, mixing rigid and flexible structures and calcu￾late again using both the methods. Log Ps for flexible structures should be worst calculated by the Ghose–Crippen method. Extend and compare the analysis by the HINT software (see the paper <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006