Bioactivity data

The CrossFire Beilstein database is the world s largest compilation of chemical facts. This database indexes three primary data domains substances, reactions and literature. The substance domain stores structural information with aU associated facts and literature references, including chemical, physical and bioactivity data. The reaction domain details the preparation of substances, enabling scientists to investigate specific reaction pathways with reaction search queries. The literature domain includes citations, titles and abstracts, which are hyperhnked to the substance and reaction domain entries. It contains over 320 million experimental data, over 10 million reactions and data indexed from over 175 journals. 

Possible Mechanism of Allelopathlc Action of Water-Insoluble Plant Lipids. Many non-polar natural products with germination and growth regulation activities In laboratory tests are In pure form not sufficiently water soluble to account for their allelopathlc activities observed In the field. For this reason the notion exists that sterols and other non-polar plant constituents are not likely to play a role In allelopathlc actions, and It Is generally concluded that the bioactivity data observed In the laboratory are therefore coincidental. 

Compounds 13-18 were tested for acetylcholinesterase inhibition activity and it was found that compounds 13 and 14 exhibited acetylchohnesterase inhibition activity with IC values (inhibition of enzyme activity by 50%) of 17 and 13 pM, respectively. Compounds 15-18 showed moderate enzyme inhibition activity with ICj, values of 35, 80, 76, and 100 pM, respectively. This bioactivity data suggested that the higher enzyme inhibition potency of compounds 13 and 14 may hypothesized due to the presence of a tetrahydrofuran ring incorporated in their stractures. Fnrthermore, compounds 1 and 2 exhibited nearly the same bioactivity and this indicated that C-7 hydroxyl group does not play any role in enzyme inhibition activity. 

Tab. 9.4 Rat versus human bioactivity data comparison using entries from WOMBAT.2004.1 N is the number of compounds, R is the correlation coefficient, and is the fraction of explained variance...

There are two bases for the comparison of similarity and diversity methods. It is possible to compare the efficiency of methods, i.e., the resources, typically computer time and computer memory, necessary for the completion of processing. Considerations of efficiency, in particular, theoretical analyses of computational complexity, are important in that they can serve to identify methods that are unlikely to be applicable given the rapidly increasing sizes of current and planned chemical datasets. Here, however, we restrict ourselves to comparing the effectiveness of similarity and diversity methods, i.e., the extent to which a method is able to satisfy the user s requirements in terms of identifying similar or diverse sets of compounds. More specifically, we focus on evaluation criteria based on the availability of bioactivity data for the molecules that are being processed, where the data can either be qualitative, i.e., a categorical (usually binary) variable, or quantitative, i.e., a real-valued variable. The discussion here considers only the criteria that can be used for comparative studies the reader is referred elsewhere for the results of such studies. 

Alternatively, Giiner and Henry (14) have introduced the G-H score, which is a weighted average of recall and precision. The score was originally developed for evaluating the effectiveness of three-dimensional (3D) database searches but can be applied to the evaluation of any sort of search for which qualitative bioactivity data are available. Using the previous notation, the G-H score is defined to be... 

A standard assumption in QSAR studies is that the models describing the data are linear. It is from this standpoint that transformations are performed on the bioactivities to achieve linearity before construction of the models. The assumption of linearity is made for each case based on theoretical considerations or the examination of scatter plots of experimental values plotted against each predicted value where the relationship between the data points appears to be nonlinear. The transformation of the bioactivity data may be necessary if theoretical considerations specify that the relationship between the two variables... 

So why precisely should this aspect of bioactivity data be important If1000 ligands are tested exhaustively against 100 targets, or 2000 ligands are tested sporadically against 50% of available targets, why is the former preferred over the latter ... 

The described applications rely heavily on the available information sources, which usually means small-molecule databases annotated with bioactivity data. Due to the... 

Effective structure-activity relationship (SAR) generation is at the centre of any medicinal chemistry campaign. Much work has been done to devise effective methods to explain and explore SAR data for medicinal chemistry teams to drive the design cycles within drug discovery projects (1). Recent work on SAR generation highlights the commonly observed discontinuity of SAR and bioactivity data, the so-called activity cliffs (2). This also emphasises the need to empirically determine SAR for each lead... 

The correlation of bioactivity data with Eq. 1 or some relationship derived from it results, if successful, in a correlation equation called a quantitative structure activity relationship (QSAR). 

Tab. 3.2 Statistical parameters (n, number of data points r, correlation coefficient s, standard error) of regression equations relating bioactivity data [6] to experimental hydro-phobicity parameters. (Reprinted from Tab. 4 of ref. 5, with permission from Elsevier Science)...

List of active compounds with bioactivity data (IC50, EC50, Kn, etc.)... 

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