ME_expert 2.0: a heuristic decision support system for microemulsions formulation development

Abstract: Artificial neural networks (ANN) and random forest (RF) classifier were employed to model microemulsions (ME) formation. The ME quantitative and qualitative composition was the system input, whereas the decision on ME presence or absence was the output. Molecular descriptors were used to characterize qualitative composition of the ME. A large database of over 300 000 records was gathered and processed to create competent models. Sensitivity analysis of ANN was used for crucial variables selection as a data-mining procedure. Final models with 17 inputs were built both for ANN and RF. In the case of ANN, they were combined into the ensemble systems with total classification rate around 85%. For RF, no ensembles were built as they are collective systems intrinsically – the total classification rate was around 86%. Combination of both ANN and RF did not improve results. The decision support system (DSS) was coded as open source Java-based software (ME_ expert 2.0) freely available from http://sourceforge.net/projects/medss .

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