Quantitative structur-activity relationships

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

Neural networks have been proposed as an alternative way to generate quantitative structure-activity relationships [Andrea and Kalayeh 1991]. A commonly used type of neural net contains layers of units with connections between all pairs of units in adjacent layers (Figure 12.38). Each unit is in a state represented by a real value between 0 and 1. The state of a unit is determined by the states of the units in the previous layer to which it is connected and the strengths of the weights on these connections. A neural net must first be trained to perform the desired task. To do this, the network is presented with a... 

Dunn W J III, S Wold, U Edlund, S Hellberg and J Gasteiger 1984. Multivariate Structure-Activib Relationships Between Data from a Battery of Biological Tests and an Ensemble of Structur Descriptors The PLS Method. Quantitative Structure-Activity Relationships 3 131-137. 

K and G M Crippen 1986. Atomic Physicochemical Parameters for Three-dimensional Struc-directed Quantitative Structure-Activity Relationships. I. Partition Coefficients as a Measure ydrophobicity. Journal of Computational Chemistry 7 565-577. 

Z, ] McClarin, T Klein and R Langridge 1985. A Quantitative Structure-Activity Relationship and ecular Graphics Study of Carbonic Anhydrase Inhibitors. Molecular Pharmacology 27 493-498. 

Holiday J D, S R Ranade and P Willett 1995. A Fast Algorithm For Selecting Sets Of Dissimilar Molecule From Large Chemical Databases. Quantitative Structure-Activity Relationships 14 501-506. 

Hudson B D, R M Hyde, E Rahr, J Wood and J Osman 1996. Parameter Based Methods for Compoun Selection from Chemical Databases. Quantitative Structure-Activity Relationships 15 285-289. 

Poso A, R Juvonen and J Gynther 1995. Comparative Molecular Field Analysis of Compounds wii CYP2A5 Binding Affinity. Quantitative Structure-Activity Relationships 14 507-511. 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology W. Karcher, J. Devillers, Eds., Kluwer, Dordrecht (1990). 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. 

Quantitative Structure—Activity Relationships. Many quantitative stmcture—activity relationship (QSAR) studies of progestins have appeared in the Hterature and an extensive review of this work is available (174). QSAR studies attempt to correlate electronic, steric, and/or hydrophobic properties to progestational activity or receptor binding affinity. A review focusing on the problems associated with QSAR of steroids has been pubUshed (175). 

The QSAR (quantitative structure-activity relationship) approach has been considered for the identification of toxicants that bind to steroid and aryl... 

GR Marshall, CD Barry, HE Bosshard, RA Dammkoehler, DA Dunn. The conformational parameter m drug design The active analog approach. ACS Symp Ser 112 205-226, 1979. JL Fauchere, ed. QSAR Quantitative Structure-Activity Relationships m Drug Design. New York Alan R Liss, 1989, pp 177-181. 

S Hellberg, M Sjostrom, B Skagerberg, S Wold. Peptide quantitative structure-activity relationships, a multivariate approach. I Med Chem 30 1126-1135, 1987. 

GM Crippen. Quantitative structure-activity relationships by distance geometry Systematic analysis of dihydrofolate reductase inhibitors. I Med Chem 23 599-606, 1980. 

JM Sutter, SL Dixon, PC Jurs. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. I Chem Inf Comput Sci 35(I) 77-84, 1995. 

BT Luke. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(6) I279-1287, 1994. 

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. 

TA Andrea, H Kalayeh. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 34 2824-2836, 1991. 

Among others, 11 was included in a series of drugs to study quantitative structure-activity relationships (96KFZ(6)29, 98MI7, 99BMC2437). A statistically significant CoMFA model was developed for describing the... 

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