Machine Learning methods are being currently applied to every single data-based field and Computer-Aided Drug Discovery is not an exception. Computational chemists are taking advantage of the increasing amount of experimental data that is available nowadays to build models that can achieve better results than the more classical SFs above described. Far from what has been exposed for the latter, where functional forms differed in specific terms but had a common scaffold, a myriad of different functional forms can be obtained depending on the algorithm chosen; from Support Vector Machines and Random Forest to the more complex and opaque Deep Neural Networks.

Functional forms of Machine learning-based SFs, also called descriptor-based by some reviewers [2], are very similar compared to empirical SFs. However, there are three major reasons to group them in another category. First, whereas classical SFs have linear functional forms, descriptor-based SFs have different functional forms depending on the machine learning algorithm employed. Second, the number of terms tends to be extremely larger in descriptor-based SF; while it is rare to find empirical SFs with more than ten weighted terms, here hundreds of different descriptors can be exploited. And third, while individual terms in an empirical SF have an easily interpretable physical meaning, this is not necessarily the case for descriptor-based SFs, which might operate as a “black box”.

The greatest asset of this kind of SFs is also the major weakness of classical models, i.e., binding affinity prediction. The predictive power of a SF is often measured as the Pearson correlation coefficient (Rp) between the experimental binding energy and the one predicted by the model. Two conclusions can be extracted from the table below [3]. The first one is that  in descriptor-based SF largely outperform the classical approaches, even the ones with better scores. And the second is that no new relevant classical SFs have been developed since the machine learning wave raised.

Furthermore, it has been demonstrated that the predictive capacity of descriptor-based SFs tends to be improved when larger training sets are provided. In this sense, many databases are now publicly available with loads of data about ligand-protein affinities. Probably the most popular among them is PDBbind, which is updated on an annual basis, and currently stores almost 13,000 carefully curated biomolecular complexes, from which around 10,000 are protein-ligand complexes.