The Application Of Artificial Intelligence Algorithms In Bio-pharmacy

Artificial neural network(ANN) is a type of mathematical model that simulates the biological nervous system and draws on analogues of adaptive biological learning.It is known to be a powerful tool to simulate various non-linear systems.The most popular ANN architecture is the multiplayer perception that generally trains the input-output relationship using a back propagation of error algorithm.Genetic algorithm(GA) is a kind of search and optimized algorithm that has been produced from stimulated biologic heredities and long evolutionary processes of creatures.Three elemental operators of the genetic algorithm are selection,crossover,and mutation.GA is capable of global optimum searching and it is a parallel process to population change and provides intrinsic parallelism.In the first section,Back-Propagation(BP) neural network was used to predict transdermal flux(1ogK_ow) and permeation coefficient(logK_p) of the compounds through the human skin.Two ANNs had different output neuron.However,the input neurons were the same,namely molecule weight(Mr),octanol-water partition coefficient (logK_ow),hydrogen-bond donor(Hd) and hydrogen-bond acceptor(Ha).For the first model,the correlation coefficient with predicted logJ_max and actual logJ_max of the testing drugs was 0.997.For the second model,the correlation coefficient between actual logK_p and calculated logK_p and root mean squared error(RMSE) of the testing set were 0.95 and 0.37.It demonstrated that BP neural network was of great help in predicting human skin permeability.The second section was aimed to establish a model for predicting biopharmaceutics classification system(BCS).Solubility and permeability of the drug decide which BCS class it belongs to.First intrinsic solubility model and absolute bioavailability model were set up.Linear algorithm and ANN were applied and compared for the solubility model and the later was chosen for higher coefficient(R),lower RMSE and lower AIC. For human oral bioavailability model,two ANNs were established.The main difference was that the second had additional eight input parameters chosen by GA,besides the seven input parameters of the first ANN which can be explained theoretically.And the second one was selected for preferable predicting efficiency.In the BCS prediction stage, sixteen durgs were chosen and self-established solubility and bioavailability models were used for prediction.The prediction accuracy of solubility,bioavailability,and BCS class were 93.8%,81.2%and 75.0%,respectively.In the third section,BP neural network was used for predicting the elimination rate constant of amikacin in neonates.Gradient descent backpropagation neural network (GD-BP-NN),bayesian regularized backpropagation neural network(BR-BP-NN) and genetic backpropagation neural network(G-BP-NN) were established.The data of amikacin serum concentrations and clinical information of 23 neonates were used to train, validate,and test the models,the effects of gestational age(w),postnatal age(d) and body weight(kg) were analyzed.The prediction precision and running efficiency were compared between the three models.Correlation coefficient of experiment calculative data and predicted data with GD-BP-NN,BR-BP-NN and G-BP-NN for the testing set were 0.92,0.91 and 0.98,respectively.Root mean squared error(RMSE) were 0.020, 0.024 and 0.010.Under the same prediction precision,running epochs of GD-BP-NN, BR-BP-NN and G-BP-NN were 2000,219 and 82.G-BP-NN is much better in the prediction of elimination rate constant of amikacin in neonates.It can overcome the weakness of BP-NN,such as the slow training speed and the vulnerability to local area pole smallness.