1. Molecular Descriptors
C371 Fall 2004

2. INTRODUCTION
Molecular descriptors are numerical values that characterize properties of molecules
Examples:
Physicochemical properties (empirical)
Values from algorithms, such as 2D fingerprints
Vary in complexity of encoded information and in compute time

3. Descriptors for Large Data Sets
Descriptors representing properties of complete molecules
Examples: LogP, Molar Refractivity
Descriptors calculated from 2D graphs
Examples: Topological Indexes, 2D fingerprints
Descriptors requiring 3D representations
Example: Pharmacophore descriptors

4. DESCRIPTORS CALCULATED FROM 2D STRUCTURES
Simple counts of features
Lipinski Rule of Five (H bonds, MW, etc.)
Number of ring systems
Number of rotatable bonds
Not likely to discriminate sufficiently when used alone
Combined with other descriptors for best effect

5. Physicochemical Properties
Hydrophobicity
LogP – the logarithm of the partition coefficient between n-octanol and water
ClogP (Leo and Hansch) – based on small set of values from a small set of simple molecules
BioByte:
Daylight’s MedChem Help page
Isolating carbon: one not doubly or triply bonded to a heteroatom

6. ACD Labs Calculated Properties
ACD Labs values now incorporated into the CAS Registry File for millions of compounds
I-Lab:
Name generation
NMR prediction
Physical property prediction

7. Molar Refractivity MR = n2 – 1 MW -------- ----- n2 + 2 d
where n is the refractive index, d is density, and MW is molecular weight.
Measures the steric bulk of a molecule.

8. Topological Indexes
Single-valued descriptors calculated from the 2D graph of the molecule
Characterize structures according to size, degree of branching, and overall shape
Example: Wiener Index – counts the number of bonds between pairs of atoms and sums the distances between all pairs

9. Topological Indexes: Others
Molecular Connectivity Indexes
Randić (et al.) branching index
Defines a “degree” of an atom as the number of adjacent non-hydrogen atoms
Bond connectivity value is the reciprocal of the square root of the product of the degree of the two atoms in the bond.
Branching index is the sum of the bond connectivities over all bonds in the molecule.
Chi indexes – introduces valence values to encode sigma, pi, and lone pair electrons

10. Kappa Shape Indexes Characterize aspects of molecular shape
Compare the molecule with the “extreme shapes” possible for that number of atoms
Range from linear molecules to completely connected graph

11. 2D Fingerprints Two types:
One based on a fragment dictionary
Each bit position corresponds to a specific substructure fragment
Fragments that occur infrequently may be more useful
Another based on hashed methods
Not dependent on a pre-defined dictionary
Any fragment can be encoded
Originally designed for substructure searching, not for molecular descriptors

12. Atom-Pair Descriptors
Encode all pairs of atoms in a molecule
Include the length of the shortest bond-by-bond path between them
Elemental type plus the number of non-hydrogen atoms and the number of π-bonding electrons

13. BCUT Descriptors
Designed to encode atomic properties that govern intermolecular interactions
Used in diversity analysis
Encode atomic charge, atomic polarizability, and atomic hydrogen bonding ability

14. DESCRIPTORS BASED ON 3D REPRESENTATIONS
Require the generation of 3D conformations
Can be computationally time consuming with large data sets
Usually must take into account conformational flexibility
3D fragment screens encode spatial relationships between atoms, ring centroids, and planes

15. Pharmacophore Keys & Other 3D Descriptors
Based on atoms or substructures thought to be relevant for receptor binding
Typically include hydrogen bond donors and acceptors, charged centers, aromatic ring centers and hydrophobic centers
Others: 3D topographical indexes, geometric atom pairs, quantum mechanical calculations for HUMO and LUMO

16. DATA VERIFICATION AND MANIPULATION
Data spread and distribution
Coefficient of variation (standard deviation divided by the mean)
Scaling (standardization): making sure that each descriptor has an equal chance of contributing to the overall analysis
Correlations
Reducing the dimensionality of a data set: Principal Components Analysis