Traditional drug discovery is expensive, inefficient, and has huge barriers to entry. Big pharmaceutical companies continue to struggle to cope with tight margins as in-house R&D become too slow to drive substantial revenue. In fact, the lack of efficient drug discovery in the pharmaceutical industry is evident – big pharma has only released 21 “new molecular entities” in the global market in 2010, while the number of drugs entering phase 3 clinical trials was 55% below 2007.1


Figure 1: Computational drug discovery allows for the integration of computing and traditional methods to optimize the drug discovery pipeline. Image courtesy of Drug Discovery World 2009. 3

Furthermore, R&D budgets of the large pharmaceutical companies are correspondingly dwindling, probably as a result of many recent expired “big ticket” drug patents.2 These numbers don’t lie, these traditional methods are not enough in drug discovery! No wonder there is decrease in the public’s perception of the pharmaceutical industry – not enough new useful drugs are being released. While the tech world has seen advances in software and big data, many people aren’t aware of the importance of computational drug discovery and how tech can play into drug discovery.

Lack of efficient drug discovery has driven the necessity for low cost and high outcome options. The need for novel drug targets and improved drugs that pass clinical development is high, and the time between reaching clinical trials and approval for drugs is upwards of 8 years.4 Computational drug discovery is a way to answer this need, by allowing researchers to search online databases for proteins to target or to model the physical structure of a protein in order to design a compound that can become a potential drug lead. Because technology has vastly improved in the past decade, there has been an explosion in the use of computing for various steps in the drug discovery pipeline in recent years. All major pharmaceutical companies now have computational research and development departments, and many biotech companies are utilizing computational methods in order to save on resources.

Computational drug discovery can aid in several steps in the pipeline, including target identification, drug lead validation, and clinical test simulations. In fact, Assay Depot lists many companies that offer various methods of computational drug discovery services, many of which will be covered in subsequent posts. Now that we know the importance of computational drug discovery, tune in for next week’s part 2 in this blog series on computational drug discovery where we will discuss in depth the hottest and best computational drug discovery tools and technologies!



  4. Holford N, Ma SC, and Ploeger BA. (2010). Clinical trial simulation: a review. Clin Pharmacol Ther. 88: 166-182.