Deep learning (DL) is a branch of machine learning that stems from artificial neural networks, which are computational models inspired in the structure of the brain and the interconnection between the neurons. DL is able to learn representations of raw data with multiple levels of abstraction. These concepts started to be developed in the 1940s 3 but it was not until 2012 that there was a break through of the Deep Neural Networks (DNN). Since then, DL has been successfully applied in natural language processing, 5 image recognition, drug discovery 7 or computational biology. The increase of computational power by parallel computing with graphics processing units (GPU) and the improvement of optimizers and regularization techniques contributed to this resurgence, along with the development of software platforms that allow to make prototyping faster and automatically manage GPU computing, like Theano or Tensorflow. DL provides a framework for the identification of both of biological targets and biologically active compounds with desired pharmacological e↵ects. In 2012, DNN won a QSAR machine learning challenge on drug discovery and activity prediction launched by Merck, outperforming Merck's Random Forests baseline model by 14% accuracy. Since then, the application of DL to pharmaceutical problems gained popularity, although it has been mainly applied to multitask QSAR modeling. Regarding DL-based proteochemometrics, little has been done except for the work of Lenselink et al, where they compared di↵erent machine learning methods for proteochemometrics, being DNN the top performer.