Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential

Abstract

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pKa's, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme–substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as “interacting quantum atoms (IQA)” energies which are expressible into an interaction matrix of two body terms (and diagonal one body “self” terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established. © 2014 The Author and the Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Integral Sign Change Problem Check in Quantitative Structure-Activity/Property Relationships: A Tutorial

“…It is unclear what was the exact size of the descriptor pool. 28 The check level 1 for this data set was reported previously. 15 The model for the complete set i.e., that one resulting from variable selection was not published and therefore.…”

“…SCP check has been already applied by some researchers, [20][21][22][23][24] whilst for other QSAR/QSPR groups it served to make them more aware of the danger of SCP. [25][26][27][28] In this work, the previous SCP check 16 is substantially extended to Integral SCP (ISCP) check. The aim of the ISCP check is to detect false or partially deficient regression models in terms of descriptors' sign changes (in further text: the sign changes), and to identify useless or ill-constructed data sets which were used to build such models, and remedy the models and data sets whenever possible.…”

AIMLDM: A program to generate and analyze electron localization–delocalization matrices (LDMs)

“…The total population N is expressible as the sum of the total number of localized electrons (N loc ) and the sum of the total number of delocalized electrons (N deloc ) [7]:…”

“…The theory also defines localization and delocalization indices (LIs and DIs) that count the number of electrons that, on average, are localized within an atomic basin or delocalized (shared) between a pair of basins in the molecule. Since QTAIM defines "self" (localization) and "shared" (delocalization) atomic populations one can cast these indices into a convenient matrix format that represents the complete fuzzy molecular graph [4][5][6][7][8][9][10].…”

Electron affinity of tricyclic, bicyclic, and monocyclic compounds containing cyanoenones correlates with their potency as inducers of a cytoprotective enzyme

“…Recent examples demonstrate the continuing growth of predictive QSAR modeling using quantum-chemical methods for the analysis of physico-chemical properties of congeneric series of chemicals that govern their biological and medicinal properties. 18,19 In addition to pursuit for the QSAR between electron affinity and biological potency of the tri-, bi-, and monocyclic compounds, the present study has another aim, that is, to check if the AM1 quantum mechanical procedure is as relevant as DFT quantum mechanical method for this type of application. This question is still controversial, since fast AM1 calculations are frequently underrated, due to the domination of heavy DFT calculations; as stated in a Nature article: "The DFT method is the most heavily cited concept in physical sciences.…”