The discovery, design and bring-to-market of a novel small-molecule drug is a very challenging process, and very expensive in terms of money, time and effort. Computer-Assisted Drug Design (CADD) methods can help to improve and refine the identification of hits in the first steps of drug development, thus having a huge positive impact on the costs of the whole process. Traditionally, interactions between ligands and targets have been predicted in CADD through a Quantitative Structure-Activity Relationship (QSAR) approach. In QSAR, a target is fixed and only information from compounds is used for modeling and predicting binding for said target. However, the compartmentalized nature of QSAR does not allow for discovering new cross-interactions between ligand and targets for which no training data is available. Proteochemometrics modeling (PCM) is an extension of QSAR which overcomes this drawback by combining information of both ligand and protein descriptors on a supervised prediction model. PCM allows for the integration of different sources of data in one model and for the general prediction of which ligands will bind to which targets. Both PCM and QSAR usally apply machine learning (ML) techniques such as random forests, support vector machine, logistic regression or partial least squares. Following the trends in other fields and the growing availability of data, deep learning (DL) has also been increasingly and succesfully applied on bioactivity prediction, 5 specially on QSAR modeling. The application of DL to PCM followed, taking advantage of public databases and improving the descriptor representation. However, an important issue for PCM and QSAR DL models is the amount and quality of data when compared to other fields of application, since increasing the number of data samples in drug discovery is expensive and thus, often infeasible. This poses a problem, since neural networks require a large quantity of training data in order to actually learn.