Quantitative structure-activity database

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

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. 

The CHI index is reportedly a relevant parameter in quantitative structure-activity relationship (QSAR) studies [41]. With this approach, log P could be determined in the range -0.45more than 25000 compounds with excellent reproducibility (within 2 index units) and reported in a GlaxoSmithKline database [11]. Two main drawbacks were identified using this approach (i) the assumptions used in Ref [7], i.e. that S is constant for all compounds and that the system dwell volume is excluded in calculations, yield some discrepancies in the resulting log P, and (ii) the set of gradient calibration... 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

To Study interactions between proteins and drugs, an available tool is the Drug Absorption, Distribution, Metabolism, and Excretion (ADME) Associated Protein Database (see Table 1.5). The database contains information about relevant proteins, functions, similarities, substrates and hgands, tissue distributions, and other features of targets. Eor the understanding of pharmacokinetic (PK) and pharmacodynamic (PD) features, some available resources are listed in Table 1.5. For example, the Pharmacokinetic and Pharmacodynamic Resources site provides links to relevant software, courses, textbooks, and journals (see Note 5). For quantitative structure-activity relationship (QSAR), the QSAR Datasets site collects data sets that are available in a structural format (see Table 1.5). 

These databases are a rich source of information, yet they do not capture an element of interest, namely the biological endpoint there is no searchable field to identify, in a quantitative manner, what is the target-related activity of a particular compound. Such information is important if one considers that (a) not all chemotypes indexed in patent databases are indeed active - some are just patent claims with no factual basis and that (b) not aU chemotypes disclosed as active are equally active, or selective for that matter, on the target of choice. Furthermore, should one decide to pursue a certain interaction hotspot in a given ligand-receptor structure (assuming good structure-activity models are available), it would be very convenient to mine structure-activity databases for similar chemotypes to use as potential bioisosteric replacements. 

Medina-Franco, J. L., Golbraikh, A., Oloff, S., Castillo, R., Tropsha, A. (2005) Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the nearest neighbor method and QSAR-based database mining. J Comput Aided Mol Des 19, 229-242. 

Tang, W.Z. and Pierotti, A.J., WWW database of disinfection by-product properties and related QSAR information, in Handbook on Quantitative Structure Activity Relationships (QSARs) for Pollution Prevention, Toxicity Screening, Risk Assessment and Web Applications, Walker, J.D., Ed, SETAC Press, Pensacola, FL, 2000. 

Methods to predict the hydrolysis rates of organic compounds for use in the environmental assessment of pollutants have not advanced significantly since the first edition of the Lyman Handbook (Lyman et al., 1982). Two approaches have been used extensively to obtain estimates of hydrolytic rate constants for use in environmental systems. The first and potentially more precise method is to apply quantitative structure/activity relationships (QSARs). To develop such predictive methods, one needs a set of rate constants for a series of compounds that have systematic variations in structure and a database of molecular descriptors related to the substituents on the reactant molecule. The second and more widely used method is to compare the target compound with an analogous compound or compounds containing similar functional groups and structure, to obtain a less quantitative estimate of the rate constant. 

Key words Bayesian models, Collaborative Drug Discovery Tuberculosis database, Docking, Mycobacterium tuberculosis, Quantitative structure-activity relationship, Tuberculosis... 

Hudson BD, Hyde RM, Rahr E et at. (1996) Parameter based methods for compound selection from chemical databases. Quant Struct-Act Relat 15 285-289 Matter H, Schwab W, Barbier D et al. (1999) Quantitative structure-activity relationship of human neutrophil col-lagenase (MMP-8) inhibitors using comparative molecular field and X-ray structure analysis. J Med Chem 42 1908-1920... 

The U.S. Food and Drug Administration (FDA) has adapted the MultiCASE technology and developed a new system known as MultiCASE QSAR-ES (quantitative structure-activity relationships expert system). This system uses the MultiCASE program and new database modules that were developed under a Cooperative Research and Development Agreement... 

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