  We show that by using intuitive and accessible molecular features it is
possible to predict the temperature-dependent second virial coefficient of
organic and inorganic compounds using Gaussian process regression. In
particular, we built a low dimensional representation of features based on
intrinsic molecular properties, topology and physical properties relevant for
the characterization of molecule-molecule interactions. The featurization was
used to predict second virial coefficients in the interpolative regime with a
relative error $\lesssim 1\% $ and to extrapolate the prediction to
temperatures outside of the training range for each compound in the dataset
with a relative error of 2.14\%. Additionally, the model's predictive abilities
were extended to organic molecules unseen in the training process, yielding a
prediction with a relative error of 2.66\%. Therefore, apart from being robust,
the present Gaussian process regression model is extensible to a variety of
organic and inorganic compounds.
