Combinatorial library design: maximizing model-fitting compounds within matrix synthesis constraints

The use of combinatorial chemistry has become commonplace within the pharmaceutical industry. Less widespread but gaining in popularity is the derivation of activity models from the high-throughput assays of these libraries. Such models are then used as filters during the design of refined daughter libraries. The design of these second generation libraries, which efficiently test and conform to the derived activity model from the large space of virtual possibilities, remains an area of considerable research. We present here a computationally efficient method for the design of optimally dense (in model matching compounds) synthetic matrices from in silico virtual libraries.