Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs

Designing drugs is a current problem in the pharmaceutical research. 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 an ensemble of three learning algorithms namely an evolutionary artificial neural network, Takagi-Sugeno neuro-fuzzy system and an artificial neural network. The ensemble combination is optimized by a particle swarm optimization algorithm. The experimental data were obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data since the number of experimental data was low due to the costs and time durations of experimentations. Experiment results indicate that the proposed methods are efficient. Keywords Hybrid learning Á Ensemble learning Á Evolutionary neural network Á Neuro-fuzzy Á Drug design

• Type: Journal
• Year: 2007
• Where: NCA
• Authors: Ajith Abraham, Crina Grosan, Stefan Tigan
• Keywords: Artificial Neural Network, Computer Networks, Drug, NCA 2007, Particle Swarm Optimization