Design combinatorial library Neural

Problem formulations [ 1-3 ] for designing lead-generation library under different constraints belong to a class of combinatorial resource allocation problems, which have been widely studied. They arise in many different applications such as minimum distortion problems in data compression (11), facility location problems (12), optimal quadrature rules and discretization of partial differential equations (13), locational optimization problems in control theory (9), pattern recognition (14), and neural networks... 

Niculescu et al. have reported the application of a related probabilistic neural net to bioactive prediction (136). These authors investigated the connection between the data preprocessing strategy and kernel choiee on the quality of the derived models. Ajay et al. also employed Bayesian methods to design a CNS-active library (97). A neural network trained using Bayesian methods was trained on CNS-aetive and CNS-inaetive data and correctly predicted up to 92% and 71% accuracy on the aetives and inactives. They used the method to generate a small library of potentially CNS-active moleeules amenable to combinatorial synthesis. 

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