Chemoinformatics helps transform data into information and subsequently into knowledge in support of decision making. New techniques and methodologies have contributed significantly to encoding and analyzing chemical, biological, and clinical data patterns. For example, different types of neural networks (e.g., neural, deep neural, Kohonen-Self Organizing Maps (SOM), and graph-based) support multitask learning, which facilitates the exploration and exploitation of synergies between prediction tasks in complex systems. This potentially alleviates the need for system reduction or approximation, an attractive approach for holistic drug discovery and design. Furthermore, it is possible to use these new techniques and methodologies for improving graph-based pharmacophoric representations, fragment-based drug design, de novo drug design, binding energy predictions, and consensus classification models . However, there are a number of caveats associated with these approaches that must be addressed in order for them to be fully mature.