It is common knowledge that drugs have polypharmacological properties that can be explored for new insights in drug discovery. That is a given drug interacts with many different proteins and a given protein interacts with multiple drugs. The polypharmacological, promiscuous nature of pharmaceuticals can have both beneficial and detrimental consequences. This attribute can be exploited to improve drug efficacy and prevent drug resistance. In addition to the ability of chemical compounds to interact with an array of protein targets, many diseases have multiple genetic determinants, and individual genetic determinants may be involved in multiple diseases. Furthermore, protein function and expression are controlled by a regulatory network of other proteins. When targeted therapies work initially patients often develop resistance due to secondary mutations or compensation from other parts of the underlying biological network. This illustrates the potential benefits of establishing computational polypharmacology methods – discovering drugs that intentionally target multiple proteins for a beneficial therapeutic result. Many adverse drug reactions (ADRs) result from drugs interacting with non-therapeutic off-targets (unintended interactions). Animal studies during preclinical trial are not always a good indication of these adverse interactions in humans, and such adverse effects are generally not discovered until a drug has reached clinical trial or is already on the market. With the number of different proteins in humans and the genetic variations observable in the population, a full understanding of all possible interactions through experiments and clinical testing alone is not feasible, making computational investigations particularly useful and relevant.

In our digitalized, data-driven world, there is a wealth of knowledge available that is beyond the processing power of an individual researcher or even team of researchers. The abundance of available biomedical data combined with the massive computing power we have available today with leadership class supercomputers provides great opportunities to advance computational drug research. A tool that reliably predicts protein and drug binding would revolutionize the pharmaceutical industry. An accurate representation of polypharmacological networks would provide a wealth of knowledge and insights on drug repurposing, side-effect prediction, and drug efficacy. This would lead the way to personalized polypharmacological networks including individual’s genetic variations resulting in a breakthrough for precision medicine. However, there are still many obstacles to overcome when it comes to utilizing massive computational power and ensuring accuracy of our predictions.