 We are now living in times where the computations are everywhere. You take your phone, your phone has more computing capabilities than the supercomputers that I was using during my PhD more than 30 years ago. Over the last few decades, life science has transformed from a primarily experimental science to one highly dependent on bioinformatics, modelling, simulation and data in general. Now this is challenging because on the one hand all these experts just as good as many of us are on life science, we're equally untrained when it comes to computers. COE is a centre of excellence for high-performance computing. I think a very clever idea from the commission because we are facing a new generation of computers that every day are more and more and more difficult to use. BioExcel was established in 2015 as the scientific and technological innovation hub in Europe for computational biomolecular research. The vision of BioExcel is to put extreme-scale computing at the heart of life science research. This will allow us to offload a lot of the work that is currently being done experimentally to the computers. Our mission and aim are to provide better and better performing software to a large user community. We are focusing on European-based software. If we think about software in BioExcel there are like three big groups of software. One is Chromax, then there is Haddock and our role is in a kind of interface between this software, other software and the final user. Which are the BioExcel building blocks. Those building blocks are basically modules that are wrapped around a variety of software and these modules can be combined into workflows and this allows to build reproducible workflows to allow reproducible research. BioExcel is also training these users so that they can learn how to use the tools that BioExcel develops and at the same time when doing all these activities BioExcel is trying to create a community around the tools it develops and computational biomolecular research. Computational research is challenging. On the one hand it's really easy to be seduced to just producing more floating point operations to show that we're achieving a particular scaling. The way BioExcel is leading this is that we're making sure that our software is better at exploiting all the advances and the diversity we're seeing hardware. So by making software better in terms of performance by making software better in terms of facility of use by training better our users we have been increasing the impact of this kind of research. One of the strongest features of BioExcel is that we're able to cover the entire spectrum of user interactions from undergraduate students to graduate students coming to one of the workshops or summer schools we're organizing but also to industry. Somebody working in a particular pharma company and then might need a new functionality or they need professional training that goes significantly beyond standard training they need to be able to talk to the experts. Historically we had much less access both to data and computational power I think that will drive the future more data driven pharmaceutical research with molecular simulations with AI machine learning and with a lot of new data generation. Basically we really try to push the board on how we can use machine learning AI and physics based modelling to deliver clinical candidates faster. Computational strategies and technologies are very important especially in the early discovery stage of the drug delivery process because it helps accelerate the process of drug discovery especially in the hit finding and lead optimization stages. With experiments you cannot access resolution that you can with MD simulations and molecular models. I've been part of various training programs organized by BioExcel for biomolecular simulations training programs related to Gromax and BioExcel building blocks that has helped me immensely on how to model the biomolecular systems how to run the simulations and also how to automate these simulations using the different tools like BioExcel building blocks. I've learned Gromax actually couple months ago it helps me to understand the chemical nature of the peptide I'm simulating in terms of the hydrogen bond frequency the distances between the atoms so that I can say how it behaves in the solvent that it is in. We currently work on two new generation antibiotics one is called taxobactin it was recently discovered about seven years ago in this study we try to understand how taxobactin is working or killing the bacteria we want to understand this at an atomic level for which NMR is great because you can get atomic level insights to the structure and the dynamics of the drug and this is where computational studies really help us because they help us to visualize the data that we gain from NMR and input depth in HADOC to generate a complex structure of the drug and that helps us to understand how they are structurally associated with each other and also how they are bound on the surface of the bacterial membrane for example. If you look at all the software that we are developing in terms of applications you find a lot of application in the life science area but also drug design binding of drugs to proteins to develop new medicine antibody, antigen interactions but also food, so optimization of enzymes for food production and of course over the last two years all the software has been heavily used for COVID-19 related research. There are 2,500 more papers from other groups that we have enabled their work on COVID through the software we have written. COVID demonstrates why a center of excellence is needed we adapt our workflows that were at this time we were developing workflows for other things and in a week we rebuilt all these workflows and this is the advantage of the building blocks and that we can know yes what happened in future revolutions of the virus the infrastructure is now ready and can be used by anyone against without the specific knowledge. Advances in hardware development and access to these massive supercomputing resources will allow in future that by using AI, big data analytics, computational modeling and simulation that we develop much faster new drugs that we improve personalized healthcare for the benefit of society. One thing that we see now a lot and I think it's artificial intelligence a lot of it is still the black box and it's important I think for the work that we are doing for drug design to also understand things and understand how things are working. These are exceptionally challenging scientific research problems that BioXL is addressing and by addressing them we are on the one hand creating knowledge in our scientific fields but we're also making Europe the leader in computational research itself.