1. A method for determining whether one or more test molecules has a biological effect selected from the group consisting of an antibacterial effect, an antiviral effect and an anticancer effect, comprising: training on a suitably programmed computer more than three different molecular property models using at least one of a first set of training data, wherein each trained molecular property model is configured to generate a molecular property model prediction regarding a property of interest of the one or more test molecules, wherein said molecular property models are used in determining different properties of interest distinct from the biological effect; using machine learning techniques to train on a suitably programmed computer system a meta-model to predict the biological effect using a second set of training data, wherein a representation of at least one molecule in the second set of training data includes predictions for the one or more test molecules generated by said molecular property models; and establishing a prediction, using the computer implemented trained meta-model, that one or more test molecules has the biological effect based on the molecular property model predictions.
1. A method for determining whether one or more test molecules has a biological effect selected from the group consisting of an antibacterial effect, an antiviral effect and an anticancer effect, comprising: training on a suitably programmed computer more than three different molecular property models using at least one of a first set of training data, wherein each trained molecular property model is configured to generate a molecular property model prediction regarding a property of interest of the one or more test molecules, wherein said molecular property models are used in determining different properties of interest distinct from the biological effect; using machine learning techniques to train on a suitably programmed computer system a meta-model to predict the biological effect using a second set of training data, wherein a representation of at least one molecule in the second set of training data includes predictions for the one or more test molecules generated by said molecular property models; and establishing a prediction, using the computer implemented trained meta-model, that one or more test molecules has the biological effect based on the molecular property model predictions. 15. The method of claim 1 , further comprising: performing a laboratory experiment for the biological effect on the one or more test molecules predicted to have the biological effect.