1. A method for training a molecular properties model, comprising: ranking a training set of property measurements for a plurality of molecules, wherein each measurement assigns a value for a property of interest relative to a single molecule; generating a representation of the molecules included in the training set that is appropriate for a selected machine learning algorithm; providing the training set to a selected machine learning algorithm that optimizes a function of the rank order of the molecules in the training set relative to the property of interest, wherein, for at least two molecules in said training set, said function penalizes incorrectly ordered molecules when said function is evaluated on said at least two molecules; executing on a computer system the machine learning algorithm to generate a trained molecular properties model; selecting at least one additional molecule; generating a representation of the at least one additional molecule appropriate for the molecular properties model; generating on a computer system and with the molecular properties model a prediction about the at least one additional molecule regarding the property of interest; and determining the accuracy of the prediction for the at least one additional molecule by performing laboratory experimentation using a physically realized sample of the test molecule.