Engineering drug design using a multi-input multi-output neuro-fuzzy system

This article presents a multi-input multi-output (MIMO) neuro-fuzzy model for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose a neuro-fuzzy model and the performance is compared with artificial neural networks. This research used the experimental data obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. The idea is to build a multi-input - multi-output neuro-fuzzy model depicting the dependence between inputs and outputs. A first order Takagi-Sugeno type fuzzy inference system is developed and it is fine tuned using neural network learning techniques. Bootstrap techniques were used to generate more samples of data and the number of experimental data is reduced due to the costs and time durations of experimentations. We obtain in this way a better estimation of some drug parameters. Experiment results indicate that the proposed method is efficient.