Use of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silico predicted CL(int) with the in vivo CL(int) estimated by the "well-stirred" model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance.