Any approach applied to drug discovery and development by the medical community and pharmaceutical industry has a direct impact on the future availability of improved, novel, and curative therapies for patients with cancer. By definition, drug discovery is a complex learning process whereby research efforts are directed toward uncovering and assimilating new knowledge to create and develop a drug for the purpose of providing benefit to a defined patient population. Accordingly, a highly desirable technology or approach to drug discovery should facilitate both effective learning and the application of newly discovered observations that can be exploited for therapeutic benefit. However, some believe that drug discovery is largely accomplished by serendipity and therefore appropriately addressed by screening a large number of compounds. Clearly, this approach has not generated an abundance of new drugs for cancer patients and suggests that a tangibly different approach in drug discovery is warranted. We employ an alternative approach to drug discovery, which is based on the elucidation and exploitation of biological, pharmacological, and biochemical mechanisms that have not been previously recognized or fully understood. Mechanism-based drug discovery involves the combined application of physics-based computer simulations and laboratory experimentation. There is increasing evidence that agreement between simulations based on the laws of physics and experimental observations results in a higher probability that such observations are more accurate and better understood as compared with either approach used alone. Physics-based computer simulation applied to drug discovery is now considered by experts in the field to be one of the ultimate methodologies for drug discovery. However, the ability to perform truly comprehensive physics-based molecular simulations remains limited by several factors, including the enormous computer-processing power that is required to perform the formidable mathematical operations and data processing (e.g. memory bandwidth, data storage and retrieval). Another major consideration is the development of software that can generate an appropriate and increasingly complex physical representation of the atomic arrangements of biological systems. During the past 17 years, we have made tremendous progress in addressing some of these obstacles by developing and optimizing physics-based computer programs for the purpose of obtaining increasingly accurate and precise information and by improving the speed of computation. To perform physics-based simulations that involve complex systems of biological and pharmaceutical interest, we have developed methods that enable us to exceed Moore's law. This has been accomplished by parallel processing as well as other methods that have enabled us to study more complex and relevant molecular systems of interest. This paper provides an overview of our approach to drug discovery and describes a novel drug, currently in clinical development, which has directly resulted from the application of this approach.