Machine learning is a field of science that focusses on mathematically describing patterns in data. Chemometrics does this for chemical data. Examples are (nano)QSAR where structural information is related to biological activity. I studied during my PhD studies the interaction between the statistics and machine learning with how you computationally (numerically) represent the question. The right combination is not obvious and it has become common to try various modelling methods, though something with support vector machines (SVM/SVR) and more recently neural networks (deep learning) have become popular. A simpler model, however, has its benefits too and frequently not significantly worse than more complex models. That said, exploring all machine learning methods manually takes a lot of time, as each comes with its own parameters which need varying.
Georgia Tsiliki (NTUA partner in eNanoMapper), Cristian Munteany (former postdoc in our group), and others developed RRegrs, an R package to explore the various models and automatically calculate a number of statistics to allow to compare them (doi:10.1186/s13321-015-0094-2). That said, following my thesis, you must never rely on performance statistics, but the output of RRegrs may help you explore the full set of models.
Tsiliki, G., Munteanu, C. R., Seoane, J. A., Fernandez-Lozano, C., Sarimveis, H., Willighagen, E. L., Sep. 2015. RRegrs: an r package for computer-aided model selection with multiple regression models. Journal of Cheminformatics 7 (1), 46. http://dx.doi.org/10.1186/s13321-015-0094-2