Bioactive structure-activity relationships

One of the sources of the fuzziness surrounding these concepts may well be the implicit assumption in structure-activity relationship (SAR) studies that molecular structure contains (i.e. encodes) the information on the biological activity of a given compound. Such an assumption cannot be incorrect, since this would imply the fallacy of SAR studies. However, the assumption becomes misleading if not properly qualified to the effect that the molecular structure of a given compound contains only part of the information on its bioactivity. Indeed, what the structure of a compound encodes is information about the molecular features accounting... 

Certain computational methodologies such as some approaches to quantitative structure-activity relationship (QSAR) studies use 3D ligand structures [37, 38]. These methods generally assume that a bioactive conformation has been estab-Hshed for a set of molecules and that these conformers can be ahgned in a maimer that reflects the relative orientation they would adopt in a binding site. It is thus... 

Over the years, intensive studies in medicinal chemistry with regard to the structure-activity relationships of compounds being used in clinical praxis have revealed the exceptional position of heterocycles. Moreover, a multitude of bioactive natural products contain a heteroatom. Therefore, the development of reliable and efficient... 

The design of the photoprobe is based on structure-activity relationship (SAR) studies if available. Ideally the photoprobe should be bioactive over the same range as its parent compound. The next step is to synthesize the bioactive photoprobe in radiolabeled form. Similarly, non-radioactive labels (primarily biotin) can also be attached via a linker arm [18]. 

In this chapter, a short introduction to DFT and to its implementation in the so-called ab initio molecular dynamics (AIMD) method will be given first. Then, focusing mainly on our own work, applications of DFT to such fields as the definition of structure-activity relationships (SAR) of bioactive compounds, the interpretation of the mechanism of enzyme-catalyzed reactions, and the study of the physicochemical properties of transition metal complexes will be reviewed. Where possible, a case study will be examined, and other applications will be described in less detail. 

Cuba, W. and Cruciani, G. Molecular field-derived descriptors for the multivariate modelling of pharmacokinetic data, in Mdecular Modelling and Prediction of Bioactivity, Proceedings of the 12th European Symposium on Quantitative Structure-Activity Relationships (QSAR 98), Gundertofte, K. and Jorgensen, F.S. (Eds). Plenum Press, New York, 2000, 89-95. 

The underlying theory of Quantitative Structure-Activity Relationship (QSAR) is that biological activity is directly related to molecular structure. Therefore, molecules with similar structure will possess similar bioactivities for similar proteins/receptors/enzymes and the changes in structure will be represented through the changes in the bioactivities. The best general description of a QSAR model is... 

Correlating analog structure with bioactivity via quantitative structure-activity relationship (QSAR) studies... 

Amino-acid abbreviations are spelled out in Appendix V. Through a series of structure-activity relationship studies, the bioactive conformation and peptide sequences that produce undesirable biologic responses were identihed. Also identified were sequences susceptible to proteolysis, and a working-model compound that eliminated these sequences was proposed (Figure 4.6). This allowed the rational design of optimized somatostatin analogues with desirable biologic characteristics and activity and increased stability. 

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). 

For years, we have been studying QSAR (quantitative structure-activity relationship) analyses of pesticides and other bioactive compounds. In many examples, we have found a decisive role of the steric effect in determining the activity variation. In this chapter, applications of various steric constants such as E E°, Vw and STERIMOL parameters to QSAR studies mostly from our own laboratory are reviewed. 

For this discussion, bioactive peptides will be defined as peptides which interact specifically with a target macromolecular acceptor or are derived from domains involved in a critical protein-protein interaction and, therefore, can compete effectively to mimic or disrupt this bimolecular interaction. Once the structure-activity relationship of a bioactive peptide is revealed, one can identify the termini and/or positions in which introduction of a caging group will be disruptive for target recognition. Alternatively, caging the peptide in an inactive conformation can be accomplished by end-to-end or end-to-side-chain cyclization. 

Once a lead is found, we have to focus in our search of property space as rapidly as possible to understand structure-activity relationships and perhaps build pharmacophore maps. To do this, we need to design more compounds to explore ranges of properties centered around our lead. This explosion of compounds is a knowledge-gathering process which, when the compounds are tested, should increase the understanding of bioactivity. By application of the neighborhood principle to eliminate similar molecules, any molecules selected for synthesis will likely have several hundred or a thousand similar structures that are stored in a virtual library. These often represent an ideal starting point for lead follow-up. 

Substructure and keyword searches including bioactivity, potency and selectivity data, structure-activity relationships, pattern recognition, Boolean searches, and modeling... 

In the 1980s, the first works studying QDO and PDO structure-activity relationship were published describing the relations between antibacterial or radiosensitizer bioactivity and experimental and theoretical physicochemical descriptors [159-162], Afterwards, very few jobs relating to QDO and PDO structure-activity relationships were described. 

Lajiness MS (1991) Evaluation of the performance of dissimilarity selection methodology. In Silipo C, Vittoria A (eds) QSAR rational approaches to the design of bioactive compounds. Proceedings of the VIII European symposium on quantitative structure-activity relationships. Sorrento, Italy, 9-13 Sept 1990. ESCOM, Leiden, pp 201-204... 

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