Quantitative structure-activity and structureproperty relationship (QSAR/QSPR) studies are unquestionably of great importance in modern chemistry and biochemistry. The concept of QSAR/QSPR is to transform searches for compounds with desired properties using chemical intuition and experience into a mathematically quantified and computerized form. Once a correlation between structure and activity/property is found, any number of compounds, including those not yet synthesized, can be readily screened on the computer in order to select structures with the properties desired. It is then possible to select the most promising compounds to synthesize and test in the laboratory. Thus, the QSAR/QSPR approach conserves resources and accelerates the process of development of new molecules for use as drugs, materials, additives, or for any other purpose. While it is not easy to find successful structureactivity/property correlations, the recent exponential growth in the number of papers dealing with QSAR/ QSPR studies clearly demonstrates the rapid progress in this area. To obtain a significant correlation, it is crucial that appropriate descriptors be employed, whether they are theoretical, empirical, or derived from readily available experimental characteristics of the structures. Many descriptors reflect simple molecular properties and thus can provide insight into the physicochemical nature of the activity/ property under consideration. Recent progress in computational hardware and the development of efficient algorithms has assisted the routine development of molecular quantummechanical calculations. New semiempirical methods supply realistic quantum-chemical molecular quantities in a relatively short computational time frame. Quantum chemical calculations are thus an attractive source of new molecular descriptors, which can, in principle, express all of the electronic and geometric properties of molecules and their interactions. Indeed, many recent QSAR/QSPR studies have employed quantum chemical descriptors alone or in combination with conventional descriptors. Quantum chemistry provides a more accurate and detailed description of electronic effects than empirical methods.1 Quantum chemical methods can be applied to quantitative structure-activity relationships by direct derivation of electronic descriptors from the molecular wave function. In many cases it has been established that errors due to the approximate nature of quantum-chemical methods and the neglect of the solvation effects are largely transferable within structurally related series; thus, relative values of calculated descriptors can be meaningful even though their absolute values are not directly applicable.2 Moreover, electronic descriptors derived from the molecular wave function can be also partitioned on the basis of atoms or groups, allowing the description of various molecular regions separately. Most work employing quantum chemical descriptors has been carried out in the field of QSAR rather than QSPR, i.e. the descriptors have been correlated with biological activities such as enzyme inhibition activity, hallucinogenic activity, etc.3-6 In part this has been because, historically, the search for quantitative relationships with chemical structure started with the development of theoretical drug design methods. Quantum-chemical descriptors have also been reported to correlate the reactivity of organic compounds, octanol/water partition coefficients, chromatographic retention indices, and various physical properties of molecules.7-11 The present article reviews applications of quantum chemical descriptors in the development of QSAR/QSPR dealing with the chemical, physical, biochemical, and pharmacological properties of compounds.

http://depa.fquim.unam.mx/amyd//archivero/TrabajoFINAL2013-1_21514.pdf

Quantum-Chemical Descriptors in QSAR/QSPR Studies

Atomic radii are not precisely defined but are nevertheless widely used parameters in modeling and understanding molecular structure and interactions. The van der Waals radii determined by Bondi from molecular crystals and data for gases are the most widely used values, but Bondi recommended radius values for only 28 of the 44 main-group elements in the periodic table. In the present Article, we present atomic radii for the other 16; these new radii were determined in a way designed to be compatible with Bondi's scale. The method chosen is a set of two-parameter correlations of Bondi's radii with repulsive-wall distances calculated by relativistic coupled-cluster electronic structure calculations. The newly determined radii (in A) are Be, 1.53; B, 1.92; Al, 1.84; Ca, 2.31; Ge, 2.11; Rb, 3.03; Sr, 2.49; Sb, 2.06; Cs, 3.43; Ba, 2.68; Bi, 2.07; Po, 1.97; At, 2.02; Rn, 2.20; Fr, 3.48; and Ra, 2.83.

Consistent van der Waals Radii for the Whole Main Group

The van der Waals volume is a widely used descriptor in modeling physicochemical properties. However, the calculation of the van der Waals volume (VvdW) is rather time-consuming, from Bondi group contributions, for a large data set. A new method for calculating van der Waals volume has been developed, based on Bondi radii. The method, termed Atomic and Bond Contributions of van der Waals volume (VABC), is very simple and fast. The only information needed for calculating VABC is atomic contributions and the number of atoms, bonds, and rings. Then, the van der Waals volume (A3/molecule) can be calculated from the following formula:  VvdW = ∑ all atom  contributions − 5.92NB − 14.7RA − 3.8RNR (NB is the number of bonds, RA is the number of aromatic rings, and RNA is the number of nonaromatic rings). The number of bonds present (NB) can be simply calculated by NB = N − 1 + RA + RNA (where N is the total number of atoms). A simple Excel spread sheet has been made to calculate van der Waals volumes for a wide r...

Fast Calculation of van der Waals Volume as a Sum of Atomic and Bond Contributions and Its Application to Drug Compounds

Predicting plasma protein binding of drugs: a new approach.

Methods, systems, and configured storage media are provided for flexible data mirroring. In particular, the invention provides many-to-one data mirroring, including mirroring from local servers running the same or different operating systems and/or file systems at two or more geographically dispersed locations. The invention also provides one-to-many data mirroring, mirroring with or without a dedicated private telecommunications link, and mirroring with or without a dedicated server or another server at the destination(s) to assist the remote mirroring unit(s). In addition, the invention provides flexibility by permitting the use of various combinations of one or more external storage units and/or RAID units to hold mirrored data. Spoofing, SCSI and other bus emulations, and further tools and techniques are used in various embodiments of the invention.

Flexible remote data mirroring

Separation of polar compounds using carbon columns.

Prediction of pKa values of phenolic and nitrogen-containing compounds by computational chemical analysis compared to those measured by liquid chromatography

The influence of glycosylation on the drug binding of human serum albumin (HSA) was studied using HSA containing different amounts and degrees of glycosylated HSA. The drugs used were furosemide, naproxen, procaine, phenylbutazone, salicylic acid, sulphamethoxazole, tolbutamide and warfarin. The drug-HSA parameters (lognK) were measured by the ultrafiltration method, frontal analysis and a modified Hummel–Dreyer method. The modified Hummel–Dreyer method was the simplest method with high precision and required the smallest amounts of proteins. The lognK values were well correlated with the octanol-water partition coefficients; the correlation coefficients were over 0.95. The results suggested that hydrophobic interaction is the predominant force for the drug binding. The early stage of glycosylation of HSA did not significantly affect the drug-binding capacity. Generally, the binding affinity of HSA decreased, perhaps due to a conformational change or steric hindrance (except naproxen) when further glycosylation occurred. © 1998 John Wiley & Sons, Ltd.

Influence of glycosylation on the drug binding of human serum albumin.

Structure—retention correlation in liquid chromatography

The new logP values (NlogP) of phenolic and nitrogen-containing compounds based on the modified CAChe™ MOPAC calculation improved the correlation coefficient with measured logP values (MlogP) to 0.950 from 0.662 (n = 87). The best correlation coefficient was obtained between retention factors of their molecular form and NlogP values among NlogP, CAChe logP, MOPAC-BlogP, Rekker’s logP, Hansch’s logP, and MlogP. Correlation coefficients were 0.880 (n = 50) for phenolic compounds and 0.900 (n = 48) for nitrogen-containing compounds. The correlation coefficients between predicted and measured maximum retention factors were 0.883 (n = 50) for phenolic compounds and 0.891 (n = 48) for nitrogen-containing compounds. Addition of pka values predicted by partial charges of atoms calculated using CACheTM MOPAC to NlogP values could predict their retention factors in eluents at given pH. The correlation coefficient between predicted and measured retention factors of phenolic compounds at pH 8.49 was 0.927 (n = 20), a...

PREDICTION OF RETENTION FACTORS OF PHENOLIC AND NITROGEN-CONTAINING COMPOUNDS IN REVERSED-PHASE LIQUID CHROMATOGRAPHY BASED ON logP AND pKa OBTAINED BY COMPUTATIONAL CHEMICAL CALCULATION

Toshihiko Hanai

Papers

Separation of polar compounds using carbon columns.

Prediction of pKa values of phenolic and nitrogen-containing compounds by computational chemical analysis compared to those measured by liquid chromatography

Influence of glycosylation on the drug binding of human serum albumin.

Structure—retention correlation in liquid chromatography

PREDICTION OF RETENTION FACTORS OF PHENOLIC AND NITROGEN-CONTAINING COMPOUNDS IN REVERSED-PHASE LIQUID CHROMATOGRAPHY BASED ON logP AND pKa OBTAINED BY COMPUTATIONAL CHEMICAL CALCULATION