In recent years, research in Artificial Neural Networks (ANNs) has resurged, now under the Deep-Learning umbrella, and grown extremely popular due to major breakthroughs in methodological and computing capabilities. Deep-Learning methods are part of representation-learning algorithms that attempt to extract and organize discriminative information from the data. Recently reported success of DL techniques in crowd-sourced chemical biology data analysis and predictive toxicology competitions has showcased these methods as powerful tools for drug-discovery and toxicology research. Nevertheless, reported applications of Deep Learning techniques for modeling complex bioactivity data for small molecules remain still limited. In this talk I will present our recent work on optimizing feed-forward Deep Neural Nets (DNNs) hyper-parameters and performance evaluation of these methods as compared to shallow methods. In our study 48 DNNs, 24 Random Forest, 20 SVM and 6 Naïve Bayes arbitrary but reasonably selected configurations were compared employing 7 diverse bioactivity datasets assembled from ChEMBL repository combined with circular fingerprints as molecular descriptors. Our results demonstrate that DNNs are powerful modeling techniques for modeling complex bioactivity data. I will then talk about a project towards a collaborative environment where we support the automated construction, optimization, profiling, sharing, running, and reusing deep (and shallow) machine learning models.