Combinatorial chemistry and high-throughput screening are revolutionizing the process of lead discovery in the pharmaceutical industry. Large numbers of structures and vast quantities of biological assay data are quickly being accumulated, overwhelming traditional structure/activity relationship (SAR) analysis technologies. Recursive partitioning is a method for statistically determining rules that classify objects into similar categories or, in this case, structures into groups of molecules with similar potencies. SCAM is a computer program implemented to make extremely efficient use of this methodology. Depending on the size of the data set, rules explaining biological data can be determined interactively. An example data set of 1650 monoamine oxidase inhibitors exemplifies the method, yielding substructural rules and leading to general classifications of these inhibitors. The method scales linearly with the number of descriptors, so hundreds of thousands of structures can be analyzed utilizing thousands to millions of molecular descriptors. There are currently no methods to deal with statistical analysis problems of this size. An important aspect of this analysis is the ability to deal with mixtures, i.e., identify SAR rules for classes of compounds in the same data set that might be binding in different ways. Most current quantitative structure/activity relationship methods require that the compounds follow a single mechanism. Advantages and limitations of this methodology are presented.

This article describes a 10-year cooperative effort between the U.S. National Institute of Standards and Technology (NIST) and five major journals in the field of thermophysical and thermochemical properties to improve the quality of published reports of experimental data. The journals are Journal of Chemical and Engineering Data, The Journal of Chemical Thermodynamics, Fluid Phase Equilibria, Thermochimica Acta, and International Journal of Thermophysics. The history of this unique cooperation is outlined, together with an overview of software tools and procedures that have been developed and implemented to aid authors, editors, and reviewers at all stages of the publication process, including experiment

Copper and zinc toxicity to the freshwater alga Chlorella sp. was determined at a range of pH values (5.5-8.0) in a synthetic softwater (hardness 40-48 mg CaCO(3)/L). The effects of the metals on algal growth (cell division) rate were determined after 48-h exposure at pH 5.5, 6.0, 6.5, 7.0, 7.5, and 8.0. The toxicity of both metals was pH dependent. As pH decreased from 8.0 to 5.5, the copper concentration required to inhibit the algal growth rate by 50% (IC50) increased from 1.0 to 19 microg/L. For zinc, the IC50 increased from 52 to 2,700 microg/L over the same pH range. Changes in solution speciation alone did not explain the increased toxicity observed as the pH increased. Modelled Cu(2+) and Zn(2+) concentrations decreased with increasing pH, whereas toxicity was observed to increase. Measurements of extracellular (cell-bound) metal concentrations support the biotic ligand model (BLM) theory of competition between protons (H(+)) and metals for binding sites at the algal cell surface. Higher extracellular metal concentrations were observed at high pH, indicating reduced competition. Independent of pH, both extracellular and intracellular copper were directly related to growth inhibition in Chlorella sp., whereas zinc toxicity was related to cell-bound zinc only. These findings suggest that the algal cell surface may be considered as the biotic ligand in further development of a chronic BLM with microalgae. Conditional binding constants (log K) were determined experimentally (using measured intracellular metal concentrations) and theoretically (using concentration-response curves) for copper and zinc for Chlorella sp. at selected pH values. Excellent agreement was found indicating the possibility of using concentration-response data to estimate conditional metal-cell binding constants.

No abstract