Historically drug discovery and development is as old as humanity. Traditional practices of India and China are still well functioning despite of being over 5000-year-old therapies.



Modern drug development is a complex issue. First step is identification of abnormal biochemical and cellular changes caused by disease followed by identification of compounds that may specifically prevent or correct these abnormalities by interacting with specific molecular sites. When a new compound shows promise, its structure is usually modified many times to reach optimal efficacy and pharmacological properties.

There are three major components of successful design: knowledge, availability of appropriate tools and luck, with the last being predominantly important. Thus, as stated many years ago by Hugo Kubinyi, drug research is still a search for a needle in a haystack.



The drug discovery process has seen many advancements over the decades. Technology has been a key driver of such advances, with significant improvements resulting from breakthroughs in assay methods, automation, imaging, nanofluidics, and software developments. It is also predictable that the length, complexity, uncertainty, and cost of drug discovery will be addressed in the future by the increased use of AI and robotics.



Lecture will discuss current status of the rationale enzyme inhibitor design illustrating the limits, uncertainties and difficulties by examples met during our studies on design and evaluation of aminophosphonates and phosphonopeptides as inhibitors of chosen aminopeptidases. These studies have focused on precise analysis of inhibitor binding behavior towards chosen enzymes with special attention given to the mechanism-based and computer-aided (in silico) design and analysis.