Resource Optimization Technique information resources

Each screening center has medicinal and synthetic chemistry expertise in order to optimize hits identified from HTS campaigns and develop them into chemical probes. Specific capabilities vary, however typical strategies employed include parallel synthesis, computational and informatics analysis, and analytical capabilities such as LC/MS techniques. The structures of novel compounds that are prepared, their synthetic protocols, analytical data and biological data are all available, and samples of final probes developed are deposited into the MLSMR. A Working Group comprised of chemists from each center meets regularly to share information, best practices, and insure optimal use of resources. 

The SNP optimizer is based on (mixed-integer) linear programming (MILP) techniques. For a general introduction into MILP we refer to [11], An SAP APO user has no access to the mathematical MILP model. Instead, the modeling is done in notions of master data of example products, recipes, resources and transportation lanes. Each master data object corresponds to a set of constraints in the mathematical model used in the optimizer. For example, the definition of a location-product in combination with the bucket definition is translated into inventory balance constraints for describing the development of the stock level over time. Additional location-product properties have further influence on the mathematical model, e.g., whether there is a maximum stock-level for a product or whether it has a finite shelf-life. For further information on the master data expressiveness of SAP SCM we refer to [9],... 

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