Qantitative Structure Pharmacokinetic Relationship Of Antimicrobial Agents Using Neural Network

Rational drug design requires reliable prediction of both pharmacokinetic and pharmadynamic properties. Many candidate drugs were eliminated through selection because of their bad phamacokinetic property, which caused great waste. Previously the pharmaceutical researchers took much attention on the facet of pharmacodynamic. However, the pharmacokinetic was overlooked. As well known, drug plasma protein binding is an important pharmacokinetic process. So this study demonstrates the application of neural network to research the QSPR of sixty-one antimicrobial agents. Firstly, the neural network used in our study is a three-layer back propagation system network including an input layer, a hidden layer and an output layer. The input layer of the neural network is compromised by the quantum chemistry parameter, physical-chemistry parameter and molecular connectivity index. The number of all the parameter is nineteen. The output of the neural network is the pharmakokinetic property -drug plasma protein binding(DPPB) of antimicrobial agents which is derived from the experimental data .All the input data and output data are normalized between 0.1 to 0.9. Fifty-one sample are randomly selected as training pair. By trial and error, the number of the hidden neurons isdetermined as thirty; The max-epoch for the training network is 500,000; In addition, other parameter of the neural network is that: learning rate 1r=0.1; increased frequency of the learning rate lr(i)=1.08; decreased frequency of the learning rate lr(d)=0.9;momentum=0.9. Secondly, the prediction ability of the network is tested by the leave-one-out method. In this procedure one compound is omitted from training set. After training, the input values of the left-out compound are fed into the network and an output (DPPB) is obtained by repeating the experiment for all the member of the training set one at a time. The computational result demonstrates that neural network is effective for the analysis of the relativity between the structure of the compound and their plasma protein binding. Finally, the test involved prediction of the pharmacokinetic properties(DPPB) of ten compound never seen by the network. The neural network predicted values show good agreement with the experimental values. This result indicates that the neural network described in this study is proper and effective for QAPR research on the drug plasma protein binding. Furthermore, neural networks are proved roust, flexibility and fitting to solve the complex nonlinear problems including QSPR study. The result also indicates that neural networks can be a powerful tool in exploration of quantitative structure-pharmacokinetic relationship.