We present the aggregated conformal predictor (ACP), an extension to the traditional inductive conformal prediction (ICP) where several inductive conformal predictors are applied on the same training set and their individual predictions are aggregated to form a single prediction on an example. The results from applying ACP on two pharmaceutical data sets (CDK5 and GNRHR) indicate that the ACP has advantages over traditional ICP. ACP reduces the variance of the prediction region estimates and improves efficiency. Still, it is more conservative in terms of validity than ICP, indicating that there is room for further improvement of efficiency without compromising validity.