OBJECTIVE:

A computational model for predicting oral bioavailability is very important both in the early stage of drug discovery to select the promising compounds for further optimizations and in later stage to identify candidates for clinical trials. In present study, we propose a support vector machine (SVM)-based kernel learning approach carried out at a set of 511 chemically diverse compounds with known oral bioavailability values.

MATERIAL AND METHODS:

For each drug, 12 descriptors were calculated. The selection of optimal hyper-plane parameters was performed with 384 training set data and the prediction efficiency of proposed classifier was tested on 127 test set data.

RESULTS:

The overall prediction efficiency for the test set came out to be 96.85%. Youden's index and Matthew correlation index were found to be 0.929 and 0.909, respectively. The area under receiver operating curve (ROC) was found to be 0.943 with standard error 0.0253.

CONCLUSION:

The prediction model suggests that while considering chemoinformatics approaches into account, SVM-based prediction of oral bioavailability can be a significantly important tool for drug development and discovery at a preliminary level.