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Statistical and machine learning
methods are routinely used to model the relationships
between observed physical, chemical, and biological properties
of a compound and numerical representations of its chemical
structure called descriptors.
The structural descriptors considered in this context
are designed to capture different levels of structure
resolution and diverse physicochemical properties of a
compound. Descriptors can be derived from atomic, bond,
and molecular properties. |
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Molecular Descriptors |
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| ADRIANA.Code
calculates a series of molecular descriptors that can be applied in the area
of in silico discovery and optimization of new chemical entities.
The descriptors encode physicochemical, topological, geometrical and surface
properties of molecules.
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The following descriptors can be calculated:
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- Physicochemical properties including number of H bond acceptors
and H bond donors, TPSA, molecular weight, logP, logS, dipole moment and molecular polarizability
- Autocorrelation of 2D interatomic distance distributions weighted
by partial charges, electronegativities and polarizabilities [1]
- Autocorrelation of 3D interatomic distance distributions weighted
by partial charges, electronegativities and polarizabilities [1]
- Radial Distribution Functions of 3D interatomic distances weighted
by partial charges, electronegativities and polarizabilities [2]
- Autocorrelation of distances between surface points
weighted by the molecular electrostatic, hydrophobicity and hydrogen bonding potential [3]
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ADRIANA.Code
is available as .
Pipeline Pilot is distributed by SciTegic, Inc., San Diego, CA, USA.
For more information please visit . |
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ADRIANA.Code comprises the former software packages
 AUTOCORR,
PETRA_M,
RCODE and
SURFACE.
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| [1] Gasteiger, J.; Teckentrup, A.;
Terfloth, L.; Spycher, S.;
J. Phys. Org. Chem. 2003, 16, 232-245. |
| [2] Terfloth, L.; Gasteiger, J.
in "The Practice of Medicinal Chemistry", 2nd Edition,
C. G. Wermuth (Editor), Amsterdam, NL, 2003, pp. 131-145. |
| [3] Teckentrup, A.; Briem,, H.; Gasteiger, J.;
J. Chem. Inf. Comput. Sci. 2004, 44, 626-634. |
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