Proteins are one of the most important building blocks of life, they convert substrates, mediate signal transduction, and are involved in all important molecular processes. The majority of all drugs mediate their effect by activating, inhibiting or modifying protein responses, or are themselves composed of proteins. Using bioinformatics software tools, protein structures can be modeled and modified to develop new drugs. Traditionally, biophysical definitions of protein stability together with stochastic sampling have been used to predict protein structures and design new proteins. Worldwide leading in this field is the Rosetta software, a package of many academically developed methods and protocols. More recently, Deep Learning and Machine Learning methods have emerged and complement these methods, such as AlphaFold2, which predicts protein structures. Furthermore, Machine Learning is also used for drug optimization of small molecule compounds.
The task of the WG Drug Modeling and Design is the research of new methods, their application the developed methods to biological and chemical problems, and transfer of knowledge on the application of these software packages. The WG Drug Modeling and Design supports scientific exchange and training in the above-mentioned methods, e.g. through workshops, hands-on training and internships. Another task is to publicize and educate about computational modeling and design methods.