You are hereA REVIEW ON ROLE OF MOLECULAR DESCRIPTORS IN QSAR: A COMPUTATIONAL METHODS APPROACH

A REVIEW ON ROLE OF MOLECULAR DESCRIPTORS IN QSAR: A COMPUTATIONAL METHODS APPROACH


4. Brief description of some descriptors is [40]:
Molecular descriptors map the structure of the compound into a set of numerical or binary values representing various molecular properties that are deemed to be important for explaining activity. Two broad families of descriptors can be distinguished, based on the dependence on the information about 3D orientation and conformation of the molecule.

4.1. 2D-QSAR Descriptors

1. Constitutional Descriptor
Molecular weight, no. of atoms, no. of non-H atoms, no. of bonds, no. of heteroatoms, no. of multiple bonds (nBM), no. of aromatic bonds, no. of functional groups (hydroxyl, amine, aldehyde, carbonyl, nitro, nitroso, etc.), no. of rings, no. of circuits, no of H-bond donors, no of H-bond acceptors, no. of Nitrogen atoms (nN), chemical composition, sum of Kier-Hall electro topological states (Ss), mean atomic polarizability (Mp), number of rotable bonds (RBN), mean atomic Sanderson electro negativity (Me), etc. Total number of atoms in the molecule. Numbers of atoms of certain chemical identity (C, H, O, N, F, etc.) in the molecule. Numbers of certain chemical groups and functionalities in the molecule. Total number of bonds in the molecule. Numbers of single, double, triple, aromatic or other bonds in the molecule. Number of rings, number of rings divided by the total number of atoms. Number of six membered aromatic rings. Molecular weight and average atomic weight.

2. Geometrical Descriptor
Descriptors using the atomic coordinates (x, y, z) of molecules are therefore called Geometricaldescriptors.Examples: vander Waals volume, molecular surface, polar surface etc,as a consequence they usually depend on the conformation. 3D petijean shape index (PJI3), Gravitational index, Balaban index, Wiener index, etc.

3. Quantum Mechanical Descriptor
Highest occupied Molecular Orbital Energy (HOMO) , Lowest Unoccupied Molecular Orbital Energy (LUMO), Most positive charge (MPC), Least negative charge (LNC), Sum of squares of charges (SSC), Sum of square of positive charges (SSPC), Sum of square of negative charges (SSNC), Sum of positive charges (SUMPC), Sum of negative charges (SUMNC), Sum of absolute of charges (SAC), Total dipole moment (DMt), Molecular dipole moment at X-direction (DMX), Molecular dipole moment at Y-direction (DMY), Molecular dipole moment at Z direction (DMZ), Electronegativity (χ= -0.5 (HOMO-LUMO)), Electrophilicity (ω= χ2/2 η), Hardness (η = 0.5 (HOMO+ LUMO)) and Softness (S=1/ η).

4. Functional Group Descriptor
Number of total tertiary carbons (nCt), Number of H-bond acceptor atoms (nHAcc), number of total hydroxyl groups (nOH), number of unsubstituted aromatic C (nCaH), number of ethers (aromatic) (nRORPh), etc.

5. Chemical Descriptor
Log P (Octanol-water partition coefficient), Hydration Energy (HE), Polarizability (Pol), Molar refractivity (MR), Molecular volume (V) and Molecular surface area (SA).

6.Substituent Electronic Descriptors
RMSQ (Root mean square error of charges), SPQ (Sum of positive charges), SNQ (Sum of negative charges), RMSDM (Root mean square of dipole moments at any Cartesian coordinate direction), TDM (Total dipole moment), FRMS (Root mean square force that any atom in constituent molecule sees right before the optimization), FMAX (Maximum force on molecule), HOMO (Highest occupied molecular orbital), LUMO (Lowest unoccupied molecular orbital), HD (Hardness), SOF (Softness), EPH (Electrophilicity) and EN (Electronegativity).

7.Topological Descriptors
Descriptors derived from the configuration of the molecules (covalent bonding pattern). Topological indices are 2D descriptors based on graph theory concepts. Since no coordinates of atoms are used, they are in general conformationally independent, despite containing topological information about the molecule. These indices have been widely used in QSAR studies. They help to differentiate the molecules according mostly to their size, degree of branching, flexibility, and overall shape e.g., In Molecular Connectivity indices, branching index is the sum of the bond connectivity’s over all bonds in the molecule and Weiner indices Counts the number of bonds among pairs of atoms and sums the distances among all pairs.A sp3 hybridized carbon has got four valences, a sp2 carbon only three.Thus the ratio of the actual branching degree to the theoretically possible branching degree can be used as descriptor as it is related to the saturation.


4.2. 3D-QSAR Descriptors
The 3D-QSAR methodology is much more computationally complex than 2D-QSAR. Several steps are involves to obtain numerical descriptors of the compound structure.

1. The conformation of the compound has to be determined either from experimental data or molecular mechanics and then refined by minimizing the energy [41, 42].
2. The conformers in dataset have to be uniformly aligned in space.
3. The space with immersed conformer is probed computationally for various descriptors.
4. Some methods independent of the compound alignment have also been developed.

4.2.1. Alignment-Dependent 3D-QSAR Descriptors

4.2.1.1. Comparative Molecular Field Analysis
The Comparative Molecular Field Analysis (CoMFA) [43] uses electrostatic (Coulombic) and steric (vander Waals) energy fields defined by the inspected compound. The aligned molecule is placed in a 3D grid. In each point of the grid lattice a probe atom with unit charge is placed and the potentials (Coulomb and Lennard-Jones) of the energy fields are computed. Then, they serve as descriptors in further analysis, typically using partial least squares regression.

4.2.1.1.1. DRAWBACKS AND LIMITATIONS OF CoMFA
Despite of offering many advantages over classical QSAR and good performance in various practicable applications, CoMFA has several limitations as given below [44-46]:
1. Uncertainty in selection of compounds and variables.
2. Fragmented contour maps with variable selection procedures.
3. Hydrophobicity not well-quantified.
4. Cut-off limits used.
5. Low signal to noise ratio due to many useless field variables.
6. Imperfections in potential energy functions.
7. Various practicable problems with PLS.
8. Applicable only to in vitrodata.
9. Since the time of its origin in 1988, numerous applications of the CoMFA method in different fields have been published [47]. Several data set have been investigated; the first being the binding affinity of the steroid data set [48] for human corticosteroid-binding globulins (CBG) and testosterone-binding globulins (TBG). Many successful endeavors of CoMFA approach in the areas of enzyme highly sensitive to bioactive conformation, different binding modes of ligands, alignment rules and number of components.
10. Too many adjustable parameters like overall orientation, lattice placement, step size, probe atom type etc.

4.2.1.2. Comparative Molecular Similarity Indices Analysis
The Comparative Molecular Similarity Indices (CoMSIA) [49] is similar to CoMFA in the aspect of atom probing throughout the regular grid lattice in which the molecules are immersed. The similarity between probe atom and the analyzed molecule are calculated. Compared to CoMFA, CoMSIA uses a different potential function, namely the Gaussian-type function which allows for accurate information in grid points located within the molecule. Steric, electrostatic, and hydrophobic properties are then calculated; hence the probe atom has the unit hydrophobicity as additional property.

4.2.2. Alignment-Independent 3D-QSAR Descriptors

4.2.2.1. Comparative Molecular Moment Analysis
The Comparative Molecular Moment Analysis (CoMMA) [50] uses second-order moments of the mass distribution and charge distributions. The moments relate to center of the mass and center of the dipole. The CoMMA descriptors include principle moments of inertia, magnitudes of dipole moment and principle quadrupole moment. Furthermore, descriptors relating charge to mass distributions are defined, in other words, magnitudes of projections of dipole on principle moments of inertia and displacement between center of mass and center of dipole.

4.2.2.2. Weighted Holistic Invariant Molecular Descriptors
The Weighted Holistic Invariant Molecular (WHIM) [51, 52] and Molecular Surface WHIM [53] descriptors provide the invariant information by employing the principle component analysis (PCA) on the centered co-ordinates of the atoms constituting the molecule. This transforms the molecule into the space that captures the most variance. In this space, several statistics are calculated and serve as directional descriptors, including variance, proportions, symmetry and kurtosis. By combining the directional descriptors, non-directional descriptors are also defined. The contribution of each atom can be weighted by a mass, vander Waals volume, atomic electronegativity, atomic polarizability, electrotopological index of Kier and Hall and molecular electrostatic potential.

Grid-Independent Descriptors [54]
It utilizes probing of the grid with specific probes. The regions showing the most favorable energies of interaction are selected, provided that the distances between the regions are large and the probe-based energies are encoded in a way independent of the molecule's arrangement.

VolSurf
The VolSurf [55, 56] works similar to GRIND for e.g., hydrophobic interactions or hydrogen bond acceptor or donor groups. The resulting lattice boxes are used to compute the descriptors relying on volumes or surfaces of 3D contours, defined by the same value of the probe molecule interaction energy. By using various probes and cut-off values for the energy, different molecular properties can be quantified. These include e.g., molecular volume and surface, and hydrophobic and hydrophilic regions.

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