Whither QSAR?

Abstract

To see the future, we must look at the past (German proverb). A QSAR (quantitative structure-activity relationship) is a mathematical equation that relates a property of interest such as a biological potency with one or more molecular properties (called descriptors), for a series of (usually) related compounds. A good example is the correlation of the anti-inflammatory potency of a series of ringsubstituted aspirin derivatives in the rat: In equation 1, ED50 = dose required to reduce paw inflammation by 50%, P = octanol-water partition coefficient, n = number of compounds used to develop the QSAR, r 2 =coefficient of determination, and s = standard error of the model. The statistics are very good, for r 2 = 0.904 means that the QSAR model accounts for 90.4% of the variation of anti-inflammatory potency. The use of log P as sole descriptor suggests that penetration of each compound to the site of action is the controlling factor. I wonder how many hundreds of aspirin derivatives were made and tested in years gone by in the hope of finding "a better aspirin", when the above model indicates that the best possible potency (assuming the same mode of action) would be only about 1.75 times that of aspirin. In line with the sentiment expressed in the German proverb that heads this Editorial, I have recently published two papers dealing with the history of QSAR.2,3 I was amazed to discover how much early work had been done. The earliest quantitative property prediction that I could find is that of Döbereiner, who in 1816 accurately predicted the specific weight of strontium sulphate by interpolation (read-across, in modern parlance) from those of calcium and barium sulphates. Even more amazing, in a way, is the insight shown by the Roman poet and philosopher Titus Lucretius Carus (ca. 99-55 B.C.), who in his De Rerum Natura 5 wrote: We see how quickly through a colander The wines will flow; how, on the other hand,

Comparison of various methods for validity evaluation of QSAR models

“…In addition, the squared factors e.g. r 2 , negatively affects the possibility to distinguish errors in one or in another direction: overpredicted or underpredicted values; these two kinds of errors have a huge different in toxicity and regulatory evaluation.…”

“…Developing a QSAR model composed of different stages i.e., (1) collecting data from the literature, (2) calculation of parameters performed by different software packages such as Dragon software or image analysis (2D-QSAR), force field calculations based on three-dimensional structures (3D-QSAR) and etc., (3) developing the QSAR model by various statistical technique e.g. multiple linear regression, artificial neural network and partial least square, and (4) validation of the model by internal (leave one out and leave many out) and external validation [2]. There are various critical points in QSAR studies that should be considered by researchers [3].…”

QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

“…Quantitative structure-activity relationship (QSAR) is one of the useful techniques in computational studies. 21,22 It relies on mathematical relationship between biological endpoints and the structural features of the studied compounds. However, the development of a reliable QSAR model is not a trivial task and needs appropriate validation by performing rigorous statistical tests.…”

Prediction of Biopharmaceutical Drug Disposition Classification System (BDDCS) by Structural Parameters

“…A major stage in drug discovery and development is evaluating the pharmacokinetic (PK) and physicochemical (PC) properties of the candidate drugs. Computational methods have become an increasingly important part of drug design and discovery over the recent decades, used for predicting the PK and PC of a candidate drug through the use of structural parameters and their correlation which is required for an efficient use of existing drugs and effective development of new drugs [1,2].…”